This is a table of type bigram and their frequencies. Use it to search & browse the list to learn more about your study carrel.
bigram | frequency |
---|---|
et al | 9572 |
expert system | 7502 |
expert systems | 7040 |
sbref http | 1852 |
knowledge base | 1851 |
artificial intelligence | 1776 |
international conference | 1377 |
machine learning | 1262 |
neural network | 1198 |
neural networks | 1149 |
computer science | 1139 |
data mining | 1124 |
data set | 1107 |
international journal | 1107 |
decision making | 872 |
new york | 868 |
fuzzy logic | 770 |
based expert | 749 |
information systems | 745 |
feature selection | 725 |
inference engine | 718 |
fuzzy expert | 711 |
knowledge acquisition | 705 |
ieee transactions | 702 |
user overlap | 702 |
based systems | 690 |
data sets | 688 |
decision support | 667 |
time series | 636 |
fuzzy sets | 580 |
case study | 567 |
pattern recognition | 543 |
training set | 489 |
based system | 485 |
problem solving | 484 |
user interface | 477 |
based reasoning | 475 |
proposed method | 474 |
genetic algorithm | 472 |
th international | 468 |
intelligent systems | 465 |
recommender systems | 464 |
rule base | 462 |
rights reserved | 453 |
decision tree | 449 |
elsevier ltd | 449 |
training data | 448 |
figure shows | 444 |
support vector | 434 |
fuzzy set | 421 |
natural language | 399 |
knowledge representation | 391 |
supply chain | 379 |
total number | 375 |
semantic web | 373 |
experimental results | 367 |
table shows | 365 |
based approach | 362 |
rule rule | 352 |
large number | 351 |
feature extraction | 341 |
information retrieval | 339 |
related work | 329 |
future work | 323 |
standard deviation | 323 |
information technology | 323 |
membership function | 319 |
let us | 314 |
artificial neural | 312 |
operational research | 308 |
proposed approach | 307 |
two different | 305 |
corresponding author | 301 |
com locate | 300 |
objective function | 300 |
target domain | 294 |
membership functions | 292 |
knowledge engineering | 290 |
overlap levels | 290 |
decision trees | 288 |
knowledge management | 283 |
yes yes | 282 |
european journal | 279 |
sentiment analysis | 278 |
locate eswa | 276 |
system based | 270 |
domain knowledge | 270 |
instant case | 268 |
human experts | 266 |
legal expert | 263 |
knowledge discovery | 263 |
collaborative filtering | 259 |
real world | 259 |
widely used | 257 |
support system | 256 |
genetic algorithms | 256 |
support systems | 255 |
results obtained | 255 |
web service | 255 |
set theory | 254 |
lecture notes | 251 |
input data | 251 |
ieee international | 250 |
different types | 246 |
credit scoring | 243 |
computer vision | 241 |
classification accuracy | 237 |
research feed | 236 |
data points | 236 |
mutual information | 236 |
expert knowledge | 234 |
attribute values | 232 |
knowledge bases | 232 |
fault diagnosis | 228 |
first step | 228 |
proposed algorithm | 228 |
data analysis | 227 |
test cases | 227 |
com volume | 226 |
human expert | 226 |
association rules | 225 |
test set | 224 |
rough set | 224 |
web services | 224 |
alert research | 223 |
case law | 223 |
save alert | 223 |
domain experts | 223 |
input variables | 222 |
proposed system | 222 |
software engineering | 220 |
rule based | 216 |
production rules | 214 |
fuzzy inference | 213 |
university press | 213 |
system will | 213 |
de db | 212 |
image processing | 212 |
logistic regression | 212 |
fuzzy rules | 212 |
hierarchy process | 211 |
contextual information | 211 |
nearest neighbour | 210 |
literature review | 210 |
results show | 208 |
test data | 208 |
information processing | 207 |
information system | 205 |
using fuzzy | 205 |
decision makers | 203 |
local search | 203 |
operations research | 201 |
knowledge based | 200 |
technical report | 198 |
knowledge engineer | 197 |
case studies | 196 |
section presents | 195 |
next section | 194 |
analytic hierarchy | 194 |
search space | 194 |
genetic programming | 194 |
feature set | 193 |
vector machines | 191 |
section describes | 191 |
signal processing | 190 |
john wiley | 190 |
web pages | 190 |
predictive performance | 189 |
ieee trans | 188 |
future research | 188 |
least one | 188 |
decision maker | 188 |
overlap level | 186 |
front matter | 186 |
see figure | 186 |
even though | 186 |
see front | 185 |
learning algorithm | 184 |
com science | 184 |
optimal solution | 184 |
commonly used | 183 |
information science | 182 |
two types | 182 |
soft computing | 181 |
high court | 180 |
db conf | 179 |
accepted manuscript | 178 |
fuzzy systems | 178 |
backward chaining | 178 |
see fig | 178 |
domestic violence | 178 |
computational linguistics | 177 |
source domain | 176 |
see section | 175 |
learning algorithms | 175 |
fitness function | 175 |
org licenses | 174 |
fuzzy rule | 174 |
data base | 173 |
knowledge sources | 172 |
system using | 171 |
similarity measure | 171 |
based methods | 171 |
recent years | 171 |
van den | 170 |
natural justice | 169 |
recommender system | 169 |
two main | 169 |
vector machine | 168 |
construction industry | 168 |
linear regression | 167 |
web page | 165 |
publishing corporation | 164 |
linear programming | 164 |
statistically significant | 163 |
rough sets | 163 |
publishing ltd | 163 |
hindawi publishing | 162 |
open access | 161 |
employer paid | 161 |
fuzzy numbers | 161 |
shop scheduling | 160 |
hong kong | 159 |
ontology matching | 159 |
see table | 159 |
new approach | 158 |
nearest neighbor | 158 |
three different | 158 |
hidden layer | 157 |
th percentile | 156 |
cluster centers | 156 |
may also | 156 |
kin term | 156 |
language processing | 155 |
naive bayes | 155 |
corporation http | 154 |
supervised learning | 154 |
clustering algorithm | 154 |
real time | 154 |
proposed model | 153 |
making process | 153 |
group decision | 153 |
based approaches | 153 |
earthquake prediction | 153 |
criteria decision | 153 |
risk assessment | 152 |
start date | 152 |
united states | 152 |
system shell | 151 |
data sources | 151 |
sth percentile | 151 |
execution time | 150 |
classification performance | 149 |
ieee computer | 149 |
independent contractor | 149 |
mit press | 148 |
linguistic variables | 147 |
evaluation results | 147 |
user preferences | 146 |
fuzzy system | 145 |
feature space | 144 |
management system | 143 |
time complexity | 143 |
wiley sons | 142 |
scheduling problem | 142 |
obtained using | 141 |
finite element | 141 |
joint conference | 141 |
particle swarm | 141 |
machine intelligence | 141 |
kin terms | 140 |
personal copy | 140 |
approximate reasoning | 139 |
computer program | 139 |
de la | 137 |
access control | 137 |
user profile | 137 |
inference mechanism | 137 |
forward chaining | 137 |
model based | 137 |
den poel | 136 |
best solution | 136 |
inference system | 136 |
association rule | 135 |
next step | 135 |
tabu search | 135 |
different user | 135 |
deep learning | 134 |
pty ltd | 134 |
voltage stability | 134 |
system development | 134 |
test case | 133 |
th annual | 133 |
conflict resolution | 133 |
power system | 133 |
mail address | 133 |
wide range | 133 |
computational intelligence | 132 |
semweb om | 132 |
paper presents | 132 |
contents lists | 132 |
user profiles | 132 |
conf semweb | 132 |
information sciences | 131 |
system architecture | 131 |
lists available | 131 |
available online | 131 |
computer society | 130 |
please cite | 130 |
medical diagnosis | 129 |
simulated annealing | 129 |
fold cross | 129 |
maximum number | 128 |
evolutionary computation | 128 |
world wide | 128 |
control system | 127 |
aware systems | 126 |
wide web | 126 |
high level | 126 |
com http | 126 |
data engineering | 126 |
sciencedirect expert | 126 |
tick data | 126 |
neural net | 125 |
mail addresses | 124 |
information management | 124 |
using different | 124 |
ep te | 124 |
sc ri | 124 |
pattern analysis | 124 |
ri pt | 124 |
cc ep | 124 |
pdf http | 124 |
international workshop | 124 |
mining techniques | 123 |
data objects | 123 |
power systems | 123 |
belief functions | 122 |
morgan kaufmann | 122 |
based method | 122 |
real estate | 121 |
allows us | 121 |
management systems | 120 |
new knowledge | 120 |
randomly selected | 120 |
input parameters | 120 |
problem domain | 120 |
small number | 120 |
social networks | 119 |
legal reasoning | 119 |
learning process | 119 |
fault detection | 119 |
agent systems | 119 |
also used | 118 |
performance evaluation | 118 |
expert sys | 118 |
method based | 117 |
will use | 117 |
two classes | 117 |
sref http | 116 |
among others | 116 |
sensitivity analysis | 116 |
customer satisfaction | 116 |
zhang et | 115 |
determine whether | 115 |
training examples | 115 |
interest operator | 115 |
ys te | 115 |
text mining | 115 |
best results | 114 |
similarity measures | 114 |
better performance | 113 |
science journal | 113 |
better results | 113 |
one hand | 112 |
many times | 112 |
traffic calming | 112 |
higher education | 112 |
error rate | 111 |
cross validation | 111 |
learning techniques | 111 |
previous work | 111 |
blackwell publishing | 111 |
process control | 111 |
different levels | 111 |
business process | 110 |
disease diagnosis | 110 |
social network | 110 |
advanced computer | 110 |
journal http | 110 |
swarm optimization | 110 |
false positive | 110 |
known whether | 110 |
org http | 110 |
face recognition | 110 |
project management | 109 |
cloud computing | 109 |
euclidean distance | 109 |
international joint | 109 |
light framing | 109 |
initial cluster | 109 |
confusion matrix | 108 |
based algorithms | 108 |
bayesian network | 108 |
intelligent system | 108 |
special issue | 108 |
possible values | 108 |
natural extension | 107 |
hough transform | 107 |
certainty factor | 107 |
employee specification | 107 |
industrial engineering | 107 |
well known | 107 |
pareto front | 107 |
computational complexity | 107 |
previous section | 106 |
random prism | 106 |
average number | 106 |
san francisco | 106 |
ltd expert | 106 |
domain expert | 105 |
system approach | 105 |
van der | 105 |
important role | 105 |
optimization problem | 105 |
system design | 105 |
management science | 105 |
knowledge transfer | 104 |
software development | 104 |
fuzzy number | 104 |
programming language | 104 |
relevant information | 104 |
procedia computer | 104 |
may lead | 104 |
knowledge engineers | 104 |
correlation coefficient | 103 |
optimal solutions | 103 |
prediction performance | 103 |
first stage | 103 |
value functions | 103 |
bayesian networks | 102 |
markov models | 102 |
inference process | 102 |
figure illustrates | 102 |
digital library | 102 |
optimization problems | 102 |
original data | 101 |
path planning | 101 |
search keys | 101 |
computation time | 101 |
parameter values | 101 |
leading cases | 101 |
based knowledge | 101 |
working memory | 101 |
feature vector | 100 |
second stage | 100 |
cost function | 100 |
knowledge sharing | 100 |
reasoning process | 100 |
system uses | 100 |
different methods | 100 |
test results | 100 |
first one | 100 |
probability distribution | 100 |
two cases | 99 |
fraud detection | 99 |
quality management | 99 |
rule set | 99 |
berlin heidelberg | 99 |
paper proposes | 99 |
many different | 99 |
significant differences | 99 |
selection process | 99 |
table presents | 99 |
peer review | 99 |
petri nets | 98 |
domain pdf | 98 |
gene expression | 98 |
selection methods | 98 |
org dialog | 98 |
data structure | 98 |
html http | 98 |
international symposium | 98 |
three main | 98 |
legitimate expectation | 98 |
statistical significance | 98 |
will also | 98 |
training dataset | 97 |
open data | 97 |
elbow height | 97 |
structural light | 97 |
ant colony | 97 |
temporal dimension | 97 |
differential diagnosis | 97 |
proposed algorithms | 97 |
data acquisition | 97 |
hand side | 97 |
computer systems | 96 |
li li | 96 |
additional information | 96 |
intellectual capital | 96 |
flow rate | 95 |
feature vectors | 95 |
solution space | 95 |
stock market | 95 |
cnc turning | 95 |
proposed expert | 95 |
time delay | 95 |
heart disease | 95 |
matrix factorization | 95 |
different ways | 95 |
th acm | 94 |
overall accuracy | 94 |
south wales | 94 |
academic press | 94 |
linguistic variable | 94 |
wang et | 94 |
control systems | 94 |
new south | 94 |
common sense | 94 |
wu et | 94 |
health care | 94 |
make use | 94 |
processing time | 93 |
fuzzy ahp | 93 |
text categorization | 93 |
computer system | 93 |
large amount | 93 |
regression analysis | 93 |
classification results | 93 |
service composition | 93 |
data collected | 92 |
anomaly detection | 92 |
conditional probability | 92 |
data collection | 92 |
proposed framework | 92 |
regression model | 92 |
prentice hall | 92 |
takes place | 92 |
contextual dimensions | 91 |
chen et | 91 |
component analysis | 91 |
sentiment classification | 91 |
testing set | 91 |
url http | 91 |
lower probabilities | 91 |
two sets | 91 |
real data | 91 |
economy research | 90 |
based learning | 90 |
stanford university | 90 |
recommendation system | 90 |
information gain | 90 |
overall performance | 90 |
mean value | 90 |
digital economy | 90 |
raw material | 90 |
system may | 90 |
web site | 89 |
pulmonary embolism | 89 |
success factors | 89 |
data structures | 89 |
time consuming | 89 |
random forests | 89 |
significant difference | 89 |
gov pubmed | 89 |
stem school | 89 |
decision analysis | 89 |
design process | 89 |
research stem | 89 |
long term | 89 |
two approaches | 89 |
decision table | 89 |
cluster analysis | 89 |
state university | 89 |
two groups | 88 |
op en | 88 |
embedded rules | 88 |
categorical data | 88 |
lower previsions | 88 |
wiley publishing | 88 |
learning research | 88 |
background knowledge | 88 |
following sections | 87 |
quality control | 87 |
different approaches | 87 |
cambridge university | 87 |
obtained results | 87 |
new method | 87 |
prior knowledge | 87 |
evaluation criteria | 87 |
different values | 87 |
binary classification | 87 |
civil engineering | 87 |
comparative study | 87 |
classification rate | 87 |
reinforcement learning | 87 |
agent system | 86 |
load forecasting | 86 |
discriminant analysis | 86 |
prediction accuracy | 86 |
prediction error | 86 |
semantic textual | 86 |
breast cancer | 86 |
performing right | 86 |
true owner | 86 |
combinatorial optimization | 86 |
bus system | 85 |
life cycle | 85 |
chain management | 85 |
selection method | 85 |
creative commons | 85 |
logic programming | 85 |
elsevier science | 85 |
developed using | 85 |
implemented using | 85 |
text classification | 85 |
two parts | 85 |
completion time | 85 |
systems research | 85 |
classification problem | 84 |
inference rules | 84 |
development process | 84 |
threshold value | 84 |
diabetic retinopathy | 84 |
two methods | 84 |
output variable | 84 |
starting point | 84 |
output layer | 84 |
business ivorking | 84 |
different kinds | 84 |
prototype system | 84 |
hidden markov | 84 |
success rate | 84 |
output variables | 84 |
ivorking paper | 84 |
class label | 83 |
intelligent agents | 83 |
mobile devices | 83 |
algorithm based | 83 |
research interests | 83 |
available data | 83 |
ready mixed | 83 |
domain model | 83 |
electrical engineering | 83 |
topsis method | 83 |
valued dominance | 83 |
engineering applications | 83 |
risk management | 83 |
companion dimension | 82 |
first two | 82 |
diagnosis system | 82 |
relationships among | 82 |
confidence level | 82 |
systems based | 82 |
case base | 82 |
mathematical models | 82 |
lee et | 82 |
rule bases | 82 |
previous research | 82 |
learning methods | 82 |
facial expression | 81 |
take advantage | 81 |
different domains | 81 |
related works | 81 |
computer engineering | 81 |
supplier selection | 81 |
negotiation process | 81 |
db mean | 81 |
upper bound | 81 |
medical expert | 81 |
weak signals | 81 |
information extraction | 81 |
web mining | 81 |
mathematical model | 81 |
search algorithm | 81 |
processing systems | 81 |
two steps | 81 |
random fields | 81 |
information provided | 80 |
location dimension | 80 |
hybrid approach | 80 |
knowledge structure | 80 |
construction projects | 80 |
kinship terminology | 80 |
dimensional space | 80 |
belief network | 80 |
open source | 80 |
selected features | 80 |
national conference | 80 |
marketing planning | 80 |
large scale | 80 |
research journal | 79 |
end date | 79 |
hidden units | 79 |
second step | 79 |
production system | 79 |
low level | 79 |
higher level | 79 |
random forest | 79 |
previous studies | 79 |
parameter space | 79 |
credit card | 79 |
diagnostic system | 79 |
hypothetical case | 79 |
good performance | 78 |
hidden layers | 78 |
weighted average | 78 |
clustering algorithms | 78 |
see also | 78 |
based classifier | 78 |
classification model | 78 |
computer applications | 78 |
page page | 78 |
using expert | 78 |
diagnostic expert | 78 |
relative importance | 78 |
boolean algebra | 78 |
three types | 78 |
empirical study | 78 |
information theory | 77 |
time required | 77 |
volume issue | 77 |
explanation facility | 77 |
approach based | 77 |
asset location | 77 |
european conference | 77 |
nearest neighbours | 77 |
production rule | 77 |
historical data | 77 |
preference relation | 77 |
com article | 77 |
rule mining | 76 |
normal distribution | 76 |
dependent variable | 76 |
social media | 76 |
system provides | 76 |
demand reduction | 76 |
following steps | 76 |
intrusion detection | 76 |
instance selection | 76 |
end users | 76 |
four different | 76 |
fuzzy petri | 76 |
overall prediction | 76 |
acm sigkdd | 76 |
current state | 75 |
idea mining | 75 |
internal control | 75 |
results showed | 75 |
natural specification | 75 |
also known | 75 |
fault tree | 75 |
regression models | 75 |
weak signal | 75 |
often used | 75 |
varying user | 75 |
solve problems | 75 |
com journals | 75 |
access article | 75 |
yang et | 75 |
performance measures | 74 |
feature subset | 74 |
semantic similarity | 74 |
peak demand | 74 |
special case | 74 |
calculated using | 74 |
covariance matrix | 74 |
united kingdom | 74 |
simulation results | 74 |
data point | 74 |
predictive accuracy | 74 |
science article | 74 |
yield model | 74 |
system performance | 74 |
springer berlin | 74 |
search engine | 73 |
target start | 73 |
principal component | 73 |
liquid retaining | 73 |
scheduling problems | 73 |
certainty factors | 73 |
weighted sum | 73 |
preference relations | 73 |
research projects | 73 |
classification models | 73 |
conditional probabilities | 73 |
new information | 73 |
per cent | 73 |
new product | 73 |
sec sec | 73 |
scheduled start | 73 |
acm transactions | 73 |
training cases | 73 |
building expert | 73 |
loss function | 73 |
original image | 73 |
integral part | 73 |
lung cancer | 73 |
filter keywords | 73 |
imbalanced data | 73 |
network model | 73 |
fuzzy model | 73 |
maintenance operator | 72 |
applications xxx | 72 |
location description | 72 |
actual end | 72 |
annual international | 72 |
scheduled end | 72 |
date scheduled | 72 |
date target | 72 |
user interests | 72 |
kluwer academic | 72 |
relevant features | 72 |
big data | 72 |
rotating machinery | 72 |
worst case | 72 |
privacy protection | 72 |
description actual | 72 |
input variable | 72 |
training process | 72 |
recognition letters | 72 |
oriented programming | 72 |
attribute value | 72 |
domain recommendation | 72 |
system must | 71 |
knowledge source | 71 |
computational cost | 71 |
based algorithm | 71 |
mean absolute | 71 |
ishares msci | 71 |
first phase | 71 |
oxford university | 71 |
intelligent tutoring | 71 |
useful information | 71 |
several different | 71 |
evolutionary algorithms | 71 |
gradient descent | 71 |
test problems | 71 |
water quality | 71 |
user model | 71 |
new data | 71 |
credit risk | 71 |
also called | 70 |
high quality | 70 |
detailed description | 70 |
specific knowledge | 70 |
annual meeting | 70 |
lower bound | 70 |
system shells | 70 |
means clustering | 70 |
user needs | 70 |
practical applications | 70 |
data quality | 70 |
end user | 70 |
administrative decisions | 69 |
multiagent systems | 69 |
decision affected | 69 |
turning centre | 69 |
englewood cliffs | 69 |
wide variety | 69 |
means algorithm | 69 |
cited many | 69 |
evaluation metrics | 69 |
data used | 69 |
possibility theory | 69 |
time spent | 69 |
many cases | 69 |
limited number | 69 |
term interests | 69 |
social science | 69 |
roc curve | 69 |
leaf node | 69 |
ta ti | 69 |
statistical methods | 69 |
system consists | 69 |
relational database | 69 |
class class | 69 |
probability density | 69 |
long time | 68 |
research center | 68 |
evaluation process | 68 |
truth value | 68 |
opt opt | 68 |
using neural | 68 |
pearson correlation | 68 |
maximum likelihood | 68 |
classification using | 68 |
value function | 68 |
granular computing | 68 |
sensor data | 68 |
among different | 68 |
good results | 68 |
methods used | 68 |
using eq | 68 |
best performance | 68 |
data processing | 68 |
convolutional neural | 68 |
intelligent agent | 68 |
vice versa | 68 |
report id | 68 |
using data | 68 |
publicly available | 68 |
ad hoc | 68 |
information sharing | 67 |
date maintenance | 67 |
results indicate | 67 |
section discusses | 67 |
failure rate | 67 |
ambient intelligence | 67 |
integrated system | 67 |
pairwise comparison | 67 |
weighting scheme | 67 |
learning rate | 67 |
video surveillance | 67 |
two rules | 67 |
training time | 67 |
source code | 67 |
similarity value | 67 |
es shell | 67 |
one another | 67 |
graphical user | 67 |
ground truth | 67 |
based model | 67 |
density function | 67 |
clustering results | 67 |
section introduces | 66 |
link prediction | 66 |
decision rules | 66 |
system technology | 66 |
false positives | 66 |
large data | 66 |
preference relaxation | 66 |
time period | 66 |
grade structural | 66 |
standard deviations | 66 |
cial intelligence | 66 |
arrival time | 66 |
ca ti | 66 |
possibility measures | 66 |
key words | 66 |
work surface | 66 |
la ck | 66 |
based cf | 66 |
time interval | 65 |
markov model | 65 |
human expertise | 65 |
research work | 65 |
input vector | 65 |
second international | 65 |
neural information | 65 |
significance level | 65 |
based models | 65 |
file system | 65 |
feature values | 65 |
classification problems | 65 |
cognitive science | 65 |
intelligence techniques | 65 |
global optimization | 65 |
expert estimations | 65 |
decision information | 64 |
fall detection | 64 |
electronic commerce | 64 |
input layer | 64 |
image annotation | 64 |
data source | 64 |
diagnosis using | 64 |
first layer | 64 |
different classes | 64 |
repertory grid | 64 |
validation set | 64 |
independent variables | 64 |
ow nl | 64 |
based decision | 64 |
cutting parameters | 64 |
hierarchical structure | 64 |
using two | 64 |
us consider | 64 |
results using | 64 |
ddos attacks | 64 |
oa de | 64 |
height plus | 64 |
contextual dimension | 64 |
petri net | 64 |
nl oa | 64 |
recognition using | 64 |
user may | 64 |
preprint submitted | 64 |
finite set | 64 |
computer programs | 64 |
ea framework | 64 |
similar users | 64 |
many researchers | 63 |
average value | 63 |
vector space | 63 |
statistical analysis | 63 |
experimental design | 63 |
detection system | 63 |
search process | 63 |
data model | 63 |
belief networks | 63 |
input output | 63 |
decision theory | 63 |
case based | 63 |
methods based | 63 |
based feature | 63 |
just one | 63 |
acm conference | 63 |
different data | 63 |
ubiquitous computing | 63 |
two major | 63 |
wavelet transform | 63 |
short term | 63 |
problem description | 63 |
sequential patterns | 63 |
using genetic | 62 |
significantly better | 62 |
also shows | 62 |
recognition rate | 62 |
raw data | 62 |
travel time | 62 |
made available | 62 |
initial population | 62 |
commons attribution | 62 |
org tr | 62 |
back propagation | 62 |
two ways | 62 |
thigh width | 62 |
mathematical programming | 62 |
observe natural | 62 |
vector regression | 62 |
programming languages | 62 |
three levels | 62 |
outlier detection | 62 |
acm international | 62 |
data type | 62 |
corehtml pmc | 62 |
maximum value | 62 |
li et | 62 |
liu et | 62 |
following two | 62 |
based fuzzy | 62 |
service quality | 62 |
model parameters | 62 |
high degree | 62 |
ordered choice | 61 |
user modeling | 61 |
gold standard | 61 |
singular value | 61 |
valued information | 61 |
domain ontology | 61 |
pattern matching | 61 |
event log | 61 |
computational time | 61 |
ai magazine | 61 |
first time | 61 |
data using | 61 |
correlation coefficients | 61 |
application domain | 61 |
survival analysis | 61 |
work performed | 61 |
unusual trip | 61 |
xp er | 61 |
search results | 61 |
make decisions | 61 |
pervasive computing | 61 |
information available | 60 |
chain ladder | 60 |
end use | 60 |
volume hindawi | 60 |
semantic analysis | 60 |
edit distance | 60 |
different aspects | 60 |
visual basic | 60 |
three steps | 60 |
better understanding | 60 |
table summarizes | 60 |
correctly classified | 60 |
possible solutions | 60 |
data envelopment | 60 |
linguistic terms | 60 |
optimization algorithm | 60 |
target domains | 60 |
ideal solution | 60 |
classification rules | 60 |
performs better | 60 |
action recognition | 60 |
academic advising | 60 |
modal split | 60 |
trip pattern | 60 |
ideal point | 60 |
complex systems | 60 |
figure figure | 60 |
envelopment analysis | 60 |
published version | 60 |
flow shop | 60 |
elementary propositions | 59 |
domain algorithms | 59 |
fuzzy reasoning | 59 |
hidden variables | 59 |
weather forecast | 59 |
organizing map | 59 |
triangular fuzzy | 59 |
none none | 59 |
main objective | 59 |
teaching materials | 59 |
decision process | 59 |
best performing | 59 |
machine tool | 59 |
color features | 59 |
systems engineering | 59 |
run time | 59 |
report file | 59 |
will provide | 59 |
nd ed | 59 |
computers operations | 59 |
exchange rate | 59 |
employee area | 59 |
end end | 58 |
validation process | 58 |
choice models | 58 |
concept embedding | 58 |
second part | 58 |
blackboard architecture | 58 |
population size | 58 |
random variable | 58 |
supreme court | 58 |
expert support | 58 |
cars evaluation | 58 |
th european | 58 |
side information | 58 |
control knowledge | 58 |
paper describes | 58 |
cosine similarity | 58 |
first case | 58 |
missing values | 58 |
also provides | 58 |
rogue femtocells | 58 |
es software | 58 |
load balancing | 58 |
data groups | 58 |
may occur | 58 |
routing problem | 58 |
ge nc | 58 |
search key | 58 |
active user | 57 |
article pii | 57 |
second one | 57 |
hindawi www | 57 |
prediction model | 57 |
business school | 57 |
pid controller | 57 |
scientific research | 57 |
initial solution | 57 |
rule induction | 57 |
factor analysis | 57 |
two kinds | 57 |
test sets | 57 |
worth noting | 57 |
next generation | 57 |
last two | 57 |
criteria weights | 57 |
image analysis | 57 |
content management | 57 |
domain specific | 57 |
ecm performance | 57 |
current situation | 57 |
view excerpt | 57 |
use case | 57 |
academic publishers | 56 |
critical success | 56 |
dependent cost | 56 |
amino acid | 56 |
label ranking | 56 |
management information | 56 |
semantic annotation | 56 |
information content | 56 |
paper presented | 56 |
section provides | 56 |
solving problems | 56 |
percentile standing | 56 |
business processes | 56 |
two nodes | 56 |
high number | 56 |
selection problem | 56 |
chemical engineering | 56 |
ellipse center | 56 |
partially supported | 56 |
operating system | 56 |
false negative | 56 |
db var | 56 |
last decade | 56 |
objective functions | 56 |
decision tables | 56 |
performed using | 56 |
operating conditions | 56 |
lin et | 56 |
load shedding | 56 |
second phase | 56 |
english high | 56 |
detection using | 55 |
multiple criteria | 55 |
complex problems | 55 |
consensus model | 55 |
workplace design | 55 |
nd international | 55 |
control strategy | 55 |
nd license | 55 |
ieee conference | 55 |
must also | 55 |
research group | 55 |
activation function | 55 |
high accuracy | 55 |
phd thesis | 55 |
real life | 55 |
model using | 55 |
word sense | 55 |
third international | 55 |
location dimensions | 55 |
research questions | 55 |
pu ta | 55 |
confidence factor | 55 |
future works | 55 |
take place | 55 |
elementary classes | 55 |
construction phase | 55 |
medical informatics | 55 |
american society | 55 |
pairwise comparisons | 55 |
li ca | 55 |
concluding remarks | 55 |
domain theory | 55 |
will allow | 55 |
element analysis | 55 |
plos one | 55 |
section concludes | 55 |
specified beforehand | 54 |
copyright act | 54 |
first order | 54 |
revised manuscript | 54 |
world applications | 54 |
root node | 54 |
radial basis | 54 |
om pu | 54 |
graphical representation | 54 |
li ge | 54 |
ensemble learning | 54 |
xu et | 54 |
per day | 54 |
control group | 54 |
confidence intervals | 54 |
prototype expert | 54 |
genetic operators | 54 |
perform well | 54 |
main goal | 54 |
research methodology | 54 |
decide whether | 54 |
research society | 54 |
concept drift | 54 |
hierarchical clustering | 54 |
nearest neighbors | 54 |
query expansion | 54 |
expression data | 54 |
computed using | 54 |
also includes | 54 |
th int | 54 |
ieee intelligent | 54 |
classification algorithms | 54 |
monte carlo | 54 |
topic models | 54 |
overall predictive | 54 |
generated using | 54 |
fuzzy relations | 53 |
acquisition process | 53 |
org wiki | 53 |
basis function | 53 |
ig na | 53 |
human behavior | 53 |
frequently used | 53 |
present study | 53 |
learning approach | 53 |
research methods | 53 |
public health | 53 |
skin lesion | 53 |
pp li | 53 |
job shop | 53 |
using artificial | 53 |
given set | 53 |
clustering method | 53 |
en te | 53 |
transclosure algorithm | 53 |
predicate logic | 53 |
fuzzy knowledge | 53 |
acquisition system | 53 |
least squares | 53 |
training sets | 53 |
basic idea | 53 |
user interfaces | 53 |
production line | 53 |
gini index | 53 |
el li | 53 |
great deal | 53 |
roc curves | 53 |
object recognition | 53 |
irrigation schedule | 53 |
th th | 53 |
san diego | 53 |
distance measure | 53 |
johnson su | 53 |
resource management | 53 |
evaluation function | 53 |
false alarm | 53 |
average time | 53 |
working paper | 53 |
edge points | 53 |
based inference | 53 |
si nc | 53 |
profit loss | 53 |
two distinct | 53 |
time intervals | 53 |
granular system | 53 |
ea rc | 53 |
user ratings | 53 |
na ls | 53 |
nt el | 53 |
cost less | 53 |
es ea | 53 |
system output | 53 |
mentioned earlier | 53 |
product development | 53 |
planning process | 53 |
programming model | 53 |
selection algorithm | 53 |
proposed es | 52 |
diagnosis systems | 52 |
vehicle routing | 52 |
regression method | 52 |
closely related | 52 |
hybrid model | 52 |
final decision | 52 |
will make | 52 |
will help | 52 |
mining approach | 52 |
various types | 52 |
tutoring systems | 52 |
brier score | 52 |
business intelligence | 52 |
worker neither | 52 |
dynamic knowledge | 52 |
document frequency | 52 |
recommendation systems | 52 |
knowledge extraction | 52 |
lane lane | 52 |
information regarding | 52 |
textual patterns | 52 |
manufacturing systems | 52 |
results presented | 52 |
associate professor | 52 |
feature sets | 52 |
book domain | 52 |
proposed methods | 52 |
percentile sitting | 52 |
direct marketing | 52 |
one may | 52 |
semantic scholar | 52 |
specific domain | 52 |
pragmatic approach | 52 |
conserved regions | 52 |
probability measure | 52 |
hepar system | 52 |
background save | 52 |
best classification | 52 |
data distribution | 52 |
nl query | 51 |
latent semantic | 51 |
given time | 51 |
possibility distribution | 51 |
popliteal height | 51 |
training instances | 51 |
absolute error | 51 |
based control | 51 |
square error | 51 |
extraction method | 51 |
new case | 51 |
intuitionistic fuzzy | 51 |
customer relationship | 51 |
randomly generated | 51 |
financial distress | 51 |
true positive | 51 |
weight values | 51 |
rated items | 51 |
dimensionality reduction | 51 |
acm press | 51 |
last years | 51 |
dc ga | 51 |
fuzzy rough | 51 |
domain level | 51 |
models based | 51 |
acquisition table | 51 |
web server | 51 |
th ieee | 51 |
proposed methodology | 51 |
sensor networks | 51 |
learning method | 51 |
high dimensional | 51 |
two categories | 51 |
calming strategies | 51 |
cluster center | 51 |
informed port | 51 |
may cause | 51 |
first part | 51 |
standing elbow | 51 |
autonomous agents | 51 |
performance metrics | 50 |
formal concept | 50 |
techniques used | 50 |
knowledge level | 50 |
leaf nodes | 50 |
risk factors | 50 |
systems using | 50 |
minimum cost | 50 |
employ others | 50 |
target variable | 50 |
sigkdd international | 50 |
available information | 50 |
power demand | 50 |
one way | 50 |
dispute resolution | 50 |
influence diagram | 50 |
fuzzy control | 50 |
new variant | 50 |
shopping centres | 50 |
rlq pain | 50 |
may result | 50 |
based classification | 50 |
first international | 50 |
unsupervised learning | 50 |
gpr ctdp | 50 |
cluster strings | 50 |
food aid | 50 |
user input | 50 |
tax instalments | 50 |
science department | 50 |
australasian performing | 50 |
local optima | 50 |
running time | 50 |
coherent lower | 50 |
data bases | 50 |
contextual values | 50 |
grading rules | 50 |
heart rate | 50 |
paye tax | 50 |
two agents | 50 |
many applications | 50 |
different groups | 50 |
annual conference | 50 |
makes use | 50 |
nk cells | 50 |
bus voltage | 50 |
worker cost | 50 |
applied soft | 50 |
rules generated | 50 |
constructed using | 50 |
user stereotype | 50 |
intelligent data | 50 |
particular problem | 49 |
base classifiers | 49 |
matching algorithm | 49 |
conventional interest | 49 |
species composition | 49 |
image segmentation | 49 |
pubmed http | 49 |
fitness value | 49 |
web application | 49 |
lead time | 49 |
training samples | 49 |
research centre | 49 |
evaluated using | 49 |
three categories | 49 |
mapping study | 49 |
three methods | 49 |
immune system | 49 |
linear combination | 49 |
threshold values | 49 |
flowshop scheduling | 49 |
dos type | 49 |
first search | 49 |
retaining structures | 49 |
scoring models | 49 |
cox regression | 49 |
flow chart | 49 |
two concepts | 49 |
term weighting | 49 |
method proposed | 49 |
different number | 49 |
based recommendation | 49 |
patient data | 49 |
level heuristics | 49 |
contextual attributes | 49 |
relatively small | 49 |
random variables | 49 |
learning models | 48 |
te rm | 48 |
process model | 48 |
machine studies | 48 |
user will | 48 |
human factors | 48 |
ellipse detection | 48 |
shape features | 48 |
transfer points | 48 |
applied mathematics | 48 |
present water | 48 |
diabetic neuropathy | 48 |
based recommender | 48 |
bench division | 48 |
first three | 48 |
using machine | 48 |
data management | 48 |
least square | 48 |
model calibration | 48 |
expert syst | 48 |
microsoft word | 48 |
mobile phone | 48 |
another example | 48 |
problem frames | 48 |
medical decision | 48 |
email addresses | 48 |
event prediction | 48 |
vessel anomaly | 48 |
correction factor | 48 |
may contain | 48 |
los angeles | 48 |
production planning | 48 |
dt models | 48 |
transportation systems | 48 |
service management | 48 |
meaning term | 48 |
engineering research | 48 |
ontology alignment | 48 |
data group | 48 |
selection criteria | 48 |
lower level | 48 |
software tools | 48 |
slightly better | 48 |
science foundation | 48 |
right association | 48 |
holiday pay | 48 |
used pilot | 48 |
high performance | 48 |
second case | 48 |
image classification | 48 |
named entity | 48 |
three groups | 47 |
blood glucose | 47 |
agent technology | 47 |
error rates | 47 |
project success | 47 |
fuzzy time | 47 |
textual information | 47 |
also considered | 47 |
past water | 47 |
product ideas | 47 |
verbal intelligence | 47 |
conditional independence | 47 |
salesman problem | 47 |
feature matrix | 47 |
may require | 47 |
method used | 47 |
computing systems | 47 |
com mailto | 47 |
sitting elbow | 47 |
square root | 47 |
rfid middleware | 47 |
affected area | 47 |
two levels | 47 |
national chiao | 47 |
traveling salesman | 47 |
fuzzy environment | 47 |
classification system | 47 |
sick pay | 47 |
org core | 47 |
difficulty level | 47 |
system developed | 47 |
existing methods | 47 |
weight vector | 47 |
parallel processing | 47 |
human resource | 47 |
multilayer perceptron | 47 |
tool selection | 47 |
monitoring system | 47 |
recent advances | 47 |
national university | 47 |
decision matrix | 47 |
total cost | 47 |
percentage points | 47 |
decision problem | 46 |
attribute weighting | 46 |
fuzzy vikor | 46 |
step length | 46 |
proposed solution | 46 |
membership values | 46 |
org package | 46 |
technical requirements | 46 |
concept analysis | 46 |
novel approach | 46 |
data fusion | 46 |
also use | 46 |
human behaviour | 46 |
topic modeling | 46 |
may include | 46 |
validation data | 46 |
proposed cd | 46 |
search strategy | 46 |
human action | 46 |
different knowledge | 46 |
tung university | 46 |
world problems | 46 |
structural analysis | 46 |
value decomposition | 46 |
property research | 46 |
ohca events | 46 |
fact plane | 46 |
reference ontology | 46 |
html documents | 46 |
parms trans | 46 |
reasonable steps | 46 |
arg max | 46 |
efficiency scores | 46 |
org abs | 46 |
also provide | 46 |
association ltd | 46 |
relations among | 46 |
traveling time | 46 |
classification framework | 46 |
overall classification | 46 |
similar results | 46 |
network structure | 46 |
engineering review | 46 |
la ss | 46 |
following equation | 46 |
fact vector | 46 |
network analysis | 46 |
model selection | 46 |
may affect | 46 |
positive rate | 46 |
permutation flowshop | 46 |
software agents | 46 |
belief function | 46 |
shopping centre | 46 |
legal analysis | 46 |
series forecasting | 46 |
competitive advantage | 46 |
probability theory | 46 |
law specifications | 46 |
pref algorithm | 46 |
current research | 46 |
tree structure | 46 |
training patterns | 46 |
mean square | 46 |
specified times | 45 |
may change | 45 |
abstract list | 45 |
inference systems | 45 |
based recsys | 45 |
fuzzy relation | 45 |
integer programming | 45 |
based techniques | 45 |
process mining | 45 |
two algorithms | 45 |
data matrix | 45 |
mobile phones | 45 |
figure presents | 45 |
space model | 45 |
numerical values | 45 |
data input | 45 |
present paper | 45 |
ra cy | 45 |
values obtained | 45 |
term component | 45 |
fuzzy linguistic | 45 |
main components | 45 |
national science | 45 |
service discovery | 45 |
network security | 45 |
texture images | 45 |
paper published | 45 |
sharpe ratio | 45 |
colony optimization | 45 |
company name | 45 |
new model | 45 |
document analysis | 45 |
de madrid | 45 |
distance measures | 45 |
ieee expert | 45 |
fire safety | 45 |
publishing co | 45 |
police officers | 45 |
water end | 45 |
sample size | 45 |
minimum support | 45 |
mean values | 45 |
db ene | 45 |
best known | 45 |
production systems | 45 |
will result | 45 |
inference methods | 45 |
new evolved | 45 |
business information | 45 |
web search | 45 |
descending order | 45 |
generally speaking | 45 |
likelihood ratio | 45 |
training sample | 45 |
research area | 45 |
pacific rim | 45 |
initial state | 45 |
cf algorithms | 45 |
will increase | 45 |
control chart | 44 |
inductive learning | 44 |
situation awareness | 44 |
sustainable development | 44 |
dopt abstract | 44 |
qos parameters | 44 |
recommended items | 44 |
may vary | 44 |
analysis systems | 44 |
uric acid | 44 |
allow us | 44 |
decision problems | 44 |
high school | 44 |
political risk | 44 |
weighting factors | 44 |
active members | 44 |
es development | 44 |
retrieve db | 44 |
pseudo code | 44 |
average precision | 44 |
uids http | 44 |
db pubmed | 44 |
learning system | 44 |
every time | 44 |
provide information | 44 |
pubmed dopt | 44 |
particular case | 44 |
regression tree | 44 |
local fitness | 44 |
list uids | 44 |
translation probabilities | 44 |
cmd retrieve | 44 |
project risk | 44 |
beef cow | 44 |
sentiment lexicons | 44 |
hybrid neural | 44 |
intelligence research | 44 |
two phases | 44 |
retrieval system | 44 |
clustering methods | 44 |
mobile computing | 44 |
gov entrez | 44 |
one rule | 44 |
lumber grading | 44 |
aza rd | 44 |
interests include | 44 |
older adults | 44 |
privy council | 44 |
principal components | 44 |
two new | 44 |
entrez query | 44 |
null hypothesis | 44 |
opinion mining | 44 |
nk cell | 44 |
gear box | 44 |
environmental impact | 44 |
important part | 44 |
process models | 44 |
one class | 44 |
much less | 44 |
optimal feature | 44 |
formal definition | 44 |
target user | 44 |
federal court | 44 |
top left | 44 |
fuzzy values | 44 |
main idea | 44 |
vh vh | 44 |
van leeuwen | 44 |
fuzzy neural | 43 |
level heuristic | 43 |
users may | 43 |
second level | 43 |
numerical data | 43 |
log file | 43 |
comparative analysis | 43 |
class labels | 43 |
reliability allocation | 43 |
dynamic programming | 43 |
mamdani fuzzy | 43 |
network process | 43 |
rogue femtocell | 43 |
previous sections | 43 |
dostupno na | 43 |
successfully applied | 43 |
clustering techniques | 43 |
database management | 43 |
uniform distribution | 43 |
specific problem | 43 |
rd international | 43 |
image interpretation | 43 |
empirical evaluation | 43 |
directed graph | 43 |
will lead | 43 |
will consider | 43 |
systems will | 43 |
upper limit | 43 |
research directions | 43 |
experimental setup | 43 |
polarimetric sar | 43 |
current solution | 43 |
realized volatility | 43 |
systems development | 43 |
industrial applications | 43 |
world congress | 43 |
second layer | 43 |
authorization specification | 43 |
selection using | 43 |
conceptual model | 43 |
wavelet packet | 43 |
business administration | 43 |
behavioural interpretation | 43 |
knowledge database | 43 |
problem solver | 43 |
kim et | 43 |
important issue | 43 |
artificial immune | 43 |
design problem | 43 |
general model | 43 |
rim property | 43 |
block diagram | 43 |
upper height | 43 |
regulated genes | 43 |
firm knot | 43 |
natural area | 43 |
hyperplane placement | 43 |
equivalence relation | 43 |
frequency domain | 43 |
huang et | 43 |
system used | 43 |
minimum number | 43 |
following section | 43 |
new text | 43 |
based summarizer | 43 |
outlier score | 43 |
baseline algorithm | 43 |
irrigation system | 43 |
regular expressions | 43 |
domain rs | 43 |
en op | 42 |
heuristic knowledge | 42 |
proposed technique | 42 |
shyster warns | 42 |
classification method | 42 |
membership value | 42 |
software packages | 42 |
real numbers | 42 |
innovation strategy | 42 |
regression function | 42 |
developing countries | 42 |
select one | 42 |
sr sr | 42 |
incremental learning | 42 |
input space | 42 |
system state | 42 |
bayes classifier | 42 |
decision variables | 42 |
draft version | 42 |
gbp jpy | 42 |
majority class | 42 |
engineering sciences | 42 |
separating hyperplane | 42 |
problem world | 42 |
power plant | 42 |
research project | 42 |
dominance decision | 42 |
different sources | 42 |
geographic location | 42 |
new variants | 42 |
econometric model | 42 |
system prototype | 42 |
eur usd | 42 |
chip processor | 42 |
objective optimization | 42 |
cars algorithms | 42 |
visual rule | 42 |
nearest result | 42 |
matching techniques | 42 |
computing time | 42 |
based information | 42 |
gypsy moth | 42 |
diagnostic problem | 42 |
joint conf | 42 |
energy management | 42 |
time window | 42 |
mutually exclusive | 42 |
human reasoning | 42 |
gbp usd | 42 |
intrusion tolerance | 42 |
shortest path | 42 |
best result | 42 |
three parts | 42 |
expectation area | 42 |
average accuracy | 42 |
table results | 42 |
service providers | 42 |
american journal | 42 |
significance tests | 42 |
dh dh | 42 |
ideal points | 42 |
based language | 42 |
rule studio | 42 |
perform better | 42 |
least two | 42 |
input values | 42 |
output data | 42 |
fourier transform | 42 |
minority class | 42 |
different models | 42 |
experimental data | 42 |
hazop analysis | 42 |
recent research | 42 |
adaptive learning | 41 |
job descriptions | 41 |
following formula | 41 |
sigir conference | 41 |
large set | 41 |
prediction using | 41 |
machine vision | 41 |
input image | 41 |
generated tests | 41 |
assistant professor | 41 |
classification approach | 41 |
evolved objects | 41 |
two years | 41 |
boolean logic | 41 |
rl rl | 41 |
new england | 41 |
sentiment detection | 41 |
process will | 41 |
features extracted | 41 |
fuzzy logical | 41 |
peak power | 41 |
network expert | 41 |
rice plant | 41 |
result set | 41 |
slightly different | 41 |
highest score | 41 |
predicted values | 41 |
much larger | 41 |
edge mask | 41 |
squared error | 41 |
ifi ca | 41 |
full text | 41 |
representation scheme | 41 |
nwareh ncustomer | 41 |
many real | 41 |
financial aid | 41 |
analysis using | 41 |
kaufmann publishers | 41 |
quite different | 41 |
will show | 41 |
acm sigir | 41 |
missing data | 41 |
workstation type | 41 |
ideal solutions | 41 |
conditional random | 41 |
fuzzy data | 41 |
idea behind | 41 |
one might | 41 |
may provide | 41 |
algorithm will | 41 |
student retention | 41 |
will become | 41 |
recommendation quality | 41 |
five years | 41 |
confidence interval | 41 |
disjunctive arcs | 41 |
mean return | 41 |
port vessel | 41 |
shyster agrees | 41 |
process planning | 41 |
two clusters | 41 |
remote sensing | 41 |
will give | 41 |
method development | 41 |
st international | 41 |
human knowledge | 41 |
percentile popliteal | 41 |
world data | 41 |
systems design | 41 |
many studies | 40 |
john dawson | 40 |
inference structure | 40 |
much better | 40 |
timbre quality | 40 |
one hidden | 40 |
edge base | 40 |
based filtering | 40 |
three kinds | 40 |
construction project | 40 |
information sources | 40 |
artificial life | 40 |
human beings | 40 |
strategic management | 40 |
two variables | 40 |
supply chains | 40 |
estimated using | 40 |
production process | 40 |
dependency tree | 40 |
edit paths | 40 |
structured som | 40 |
evaluation system | 40 |
response time | 40 |
several approaches | 40 |
consistent set | 40 |
performance assessment | 40 |
cash flow | 40 |
also included | 40 |
total quality | 40 |
contains information | 40 |
building blocks | 40 |
built using | 40 |
hierarchical fuzzy | 40 |
term map | 40 |
precision recall | 40 |
nearest others | 40 |
kalman filter | 40 |
paired stands | 40 |
new solution | 40 |
making decisions | 40 |
done using | 40 |
similarity values | 40 |
intelligent transportation | 40 |
valued logic | 40 |
di erent | 40 |
text pattern | 40 |
famous players | 40 |
new algorithm | 40 |
approach using | 40 |
three major | 40 |
nutrition care | 40 |
graph edit | 40 |
collected data | 40 |
input feature | 40 |
also show | 40 |
data streams | 40 |
previous works | 40 |
foreign exchange | 40 |
ea frameworks | 40 |
real number | 40 |
short time | 40 |
average distance | 40 |
also allows | 40 |
also important | 40 |
face images | 40 |
eswa http | 40 |
current node | 40 |
generated test | 39 |
software tool | 39 |
information obtained | 39 |
model forecasts | 39 |
feature value | 39 |
selection procedure | 39 |
survival function | 39 |
filter kc | 39 |
large amounts | 39 |
truth values | 39 |
performance analysis | 39 |
category structural | 39 |
optimal path | 39 |
speech recognition | 39 |
new rule | 39 |
ai techniques | 39 |
information contained | 39 |
references background | 39 |
system reboot | 39 |
material family | 39 |
highly correlated | 39 |
similar cases | 39 |
risk analysis | 39 |
spanish mnes | 39 |
lawrence erlbaum | 39 |
bridge tap | 39 |
five different | 39 |
method uses | 39 |
two fuzzy | 39 |
check whether | 39 |
prominent attributes | 39 |
attribute space | 39 |
simulation model | 39 |
correct predictions | 39 |
php main | 39 |
learning phase | 39 |
line removal | 39 |
information needed | 39 |
performance appraisal | 39 |
relative preference | 39 |
complete var | 39 |
use different | 39 |
near future | 39 |
start problem | 39 |
service recommendation | 39 |
intelligence engineering | 39 |
two stages | 39 |
ll rights | 39 |
older adult | 39 |
related problems | 39 |
moving average | 39 |
euclidean distances | 39 |
machine interface | 39 |
parameter estimation | 39 |
following way | 39 |
making processes | 39 |
educational systems | 39 |
computer interaction | 39 |
us assume | 39 |
intelligent information | 39 |
subset selection | 39 |
applied artificial | 39 |
software systems | 39 |
technical analysis | 39 |
technical efficiency | 39 |
sit stand | 39 |
rules may | 39 |
sciences publication | 39 |
production research | 39 |
entered port | 39 |
recognition system | 39 |
system allows | 39 |
development environment | 39 |
height minus | 39 |
var average | 39 |
also shown | 39 |
human body | 39 |
preoperative screening | 39 |
consider two | 39 |
detailed information | 39 |
ontology based | 39 |
kdd cup | 39 |
different techniques | 39 |
pattern classification | 39 |
publishing company | 39 |
across different | 39 |
noisy data | 39 |
method using | 39 |
may need | 38 |
technical university | 38 |
palo alto | 38 |
turn penalties | 38 |
cpu time | 38 |
truth maintenance | 38 |
retrieval number | 38 |
several times | 38 |
virtual activity | 38 |
main features | 38 |
reflexive tests | 38 |
transition matrix | 38 |
using adaptive | 38 |
eyes intelligence | 38 |
computational results | 38 |
dematel method | 38 |
iq index | 38 |
enables us | 38 |
please note | 38 |
will see | 38 |
dynamic environment | 38 |
prediction models | 38 |
fuzzy decision | 38 |
network learning | 38 |
ontology design | 38 |
conformance checking | 38 |
one https | 38 |
production management | 38 |
arxiv preprint | 38 |
rule activation | 38 |
propagation neural | 38 |
new feature | 38 |
fu zzy | 38 |
problem solution | 38 |
general framework | 38 |
goal programming | 38 |
problem using | 38 |
clinical decision | 38 |
based representation | 38 |
quality assessment | 38 |
best correlation | 38 |
ieee th | 38 |
regular expression | 38 |
original dataset | 38 |
insert figure | 38 |
first group | 38 |
eai endorsed | 38 |
research institute | 38 |
blue eyes | 38 |
similar patterns | 38 |
attribute decision | 38 |
class distribution | 38 |
customer behavior | 38 |
first level | 38 |
saudi arabia | 38 |
also include | 38 |
pharmaceutical industry | 38 |
duty standby | 38 |
fuzzy variables | 38 |
rule book | 38 |
word embedding | 38 |
analytical chemistry | 38 |
given two | 38 |
modes algorithm | 38 |
new rules | 38 |
feasibility study | 38 |
also proposed | 38 |
hi gh | 38 |
arg min | 38 |
average polarity | 38 |
evolutionary algorithm | 38 |
heuristic search | 38 |
interaction effects | 38 |
learning systems | 38 |
similar way | 38 |
preventive maintenance | 38 |
risk items | 38 |
mellon university | 38 |
dynamic time | 38 |
fuzzy clustering | 38 |
standby cstr | 38 |
scheduling system | 38 |
different algorithms | 38 |
based intelligent | 38 |
fuzzy cognitive | 38 |
system model | 38 |
visual privacy | 38 |
vibration signals | 38 |
risk sources | 38 |
endorsed transactions | 38 |
crack width | 38 |
heuristic programming | 38 |
positive negative | 38 |
comparison matrix | 38 |
posterior probability | 37 |
problem statement | 37 |
system applications | 37 |
first column | 37 |
markov chain | 37 |
mixture model | 37 |
factors affecting | 37 |
science research | 37 |
causally related | 37 |
right side | 37 |
exactly one | 37 |
laparoscopic gastrectomy | 37 |
prediction algorithms | 37 |
blood pressure | 37 |
polynomial time | 37 |
will take | 37 |
using rule | 37 |
bankruptcy prediction | 37 |
system also | 37 |
historical text | 37 |
hoq matrix | 37 |
taking advantage | 37 |
charge tank | 37 |
probability distributions | 37 |
application domains | 37 |
two consecutive | 37 |
one type | 37 |
search terms | 37 |
model performance | 37 |
mechanical engineering | 37 |
crossover operator | 37 |
tag suggestion | 37 |
application development | 37 |
technology classification | 37 |
last section | 37 |
dataset contains | 37 |
shoulder height | 37 |
another one | 37 |
tree analysis | 37 |
medical knowledge | 37 |
communication technologies | 37 |
training error | 37 |
io na | 37 |
rank correlation | 37 |
dynamic user | 37 |
classification task | 37 |
article title | 37 |
ss ifi | 37 |
infringing act | 37 |
wavelet filter | 37 |
energy demand | 37 |
another approach | 37 |
basic concepts | 37 |
risk exposure | 37 |
main purpose | 37 |
illustrative example | 37 |
also possible | 37 |
data entry | 37 |
performance measure | 37 |
directory structures | 37 |
surveillance systems | 37 |
computationally expensive | 37 |
state space | 37 |
library based | 37 |
wavelet decomposition | 37 |
es mailto | 37 |
block matching | 37 |
improved interest | 37 |
two points | 37 |
postf predictive | 37 |
power automaton | 37 |
two datasets | 37 |
related research | 37 |
expert rules | 37 |
crisp value | 37 |
information related | 37 |
important factors | 37 |
new jersey | 37 |
ip address | 37 |
times times | 37 |
will describe | 37 |
work presented | 37 |
tree algorithm | 37 |
important features | 37 |
relationship management | 37 |
percent error | 37 |
classification methods | 37 |
table comparison | 37 |
operations management | 37 |
based search | 37 |
deduct paye | 37 |
order tracking | 37 |
micrometeorological data | 37 |
particular domain | 37 |
organizational learning | 37 |
expression recognition | 37 |
network based | 37 |
systems use | 37 |
ap le | 37 |
measurement criteria | 37 |
performance comparison | 37 |
function value | 37 |
based neural | 36 |
integrated expert | 36 |
certain level | 36 |
computer simulation | 36 |
new problem | 36 |
user must | 36 |
iterative process | 36 |
risk estimation | 36 |
first experiment | 36 |
two stage | 36 |
jd melt | 36 |
detailed analysis | 36 |
service operations | 36 |
tire machine | 36 |
domain concepts | 36 |
semantic information | 36 |
relevant passages | 36 |
chen expert | 36 |
semantic indexing | 36 |
construction site | 36 |
using linguistic | 36 |
cell cycle | 36 |
learning environment | 36 |
hospital admission | 36 |
markov random | 36 |
prior probability | 36 |
many problems | 36 |
data classification | 36 |
query language | 36 |
data may | 36 |
low cost | 36 |
choice behaviour | 36 |
matching process | 36 |
logistics information | 36 |
adaptive fuzzy | 36 |
different forms | 36 |
proportional hazards | 36 |
user requirements | 36 |
appraisal system | 36 |
high levels | 36 |
existing systems | 36 |
commercially available | 36 |
ranked list | 36 |
belief rule | 36 |
two components | 36 |
much higher | 36 |
inference engines | 36 |
right hand | 36 |
pdf view | 36 |
search method | 36 |
assignment problem | 36 |
another important | 36 |
enterprise content | 36 |
based access | 36 |
associative classification | 36 |
nearest unknown | 36 |
whole process | 36 |
vikor method | 36 |
grade limit | 36 |
full court | 36 |
main advantage | 36 |
preprint arxiv | 36 |
unknown distance | 36 |
important aspect | 36 |
embedded rule | 36 |
boundary conditions | 36 |
knowledge reduction | 36 |
several years | 36 |
measured values | 36 |
previous step | 36 |
data obtained | 36 |
new methods | 36 |
mutation operator | 36 |
ca tio | 36 |
km km | 36 |
used may | 36 |
feature fi | 36 |
meter collectors | 36 |
also presented | 36 |
process knowledge | 36 |
classification algorithm | 36 |
benchmark data | 36 |
different sets | 36 |
possibility function | 36 |
four categories | 36 |
ruggedness test | 36 |
mean trip | 36 |
existing approaches | 36 |
approach used | 36 |
class imbalance | 36 |
trust quotient | 36 |
matching systems | 35 |
two systems | 35 |
adding new | 35 |
training phase | 35 |
two subsets | 35 |
arts heritage | 35 |
following subsections | 35 |
tu ra | 35 |
tests verified | 35 |
oo patterns | 35 |
five judges | 35 |
english court | 35 |
haza rd | 35 |
performance measurement | 35 |
two features | 35 |
powerful tool | 35 |
trading system | 35 |
processing units | 35 |
optimal number | 35 |
programming project | 35 |
mobile robot | 35 |
trading strategy | 35 |
wise preferences | 35 |
rule sets | 35 |
independent variable | 35 |
analytic network | 35 |
introduction related | 35 |
fuzzy membership | 35 |
following three | 35 |
experimental evaluation | 35 |
information fusion | 35 |
significant attributes | 35 |
main contributions | 35 |
software maintenance | 35 |
data samples | 35 |
statistical tests | 35 |
design exploration | 35 |
internal structure | 35 |
ltdexpert systems | 35 |
general problem | 35 |
smash raisebox | 35 |
linear function | 35 |
circuit pack | 35 |
four main | 35 |
svm classifier | 35 |
texture classification | 35 |
different contexts | 35 |
acm trans | 35 |
load forecast | 35 |
base contains | 35 |
table ii | 35 |
distributed systems | 35 |
technical reports | 35 |
take reasonable | 35 |
science ltd | 35 |
sense disambiguation | 35 |
following form | 35 |
associative memory | 35 |
human motion | 35 |