id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_7xjlmdv42zgjrmqnn44vauu7bu	Jakub M. Tomczak	Classification Restricted Boltzmann Machine for comprehensible credit scoring model	2015	7	.pdf	application/pdf	3149	341	61	Tomczak, Institute of Computer Science, Wroclaw University of Technology, wyb. Application of Classification Restricted Boltzmann Machine to Medical Domains which are able to extract features automatically and obtain high predictive performance. building block of a deep architecture is Restricted Boltzmann Machine (RBM). in RBM by adding a regularization term to the learning objective which enforces sparse solution. considered classifier is then applied to five different medical domains. Key words: Restricted boltzmann machine • classification • sparse • medical domain • diabetes • oncology Typical sparse learning is based on adding a regularization term to the learning objective (typically discriminative learning (Section II-B) and sparse learning with well-known classifiers in five medical A RBM with M hidden units is a parametric model Further, we will refer sparse learning of ClassRBM discriminative and sparse learning of ClassRBM give Second, in our proposition of sparse learning we The Journal of Machine Learning Research, 	./cache/work_7xjlmdv42zgjrmqnn44vauu7bu.pdf	./txt/work_7xjlmdv42zgjrmqnn44vauu7bu.txt