id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_nxdy636kujcvvj3ogvkz3iw7ca	Yongping Zhao	Rough ν-support vector regression	2009	10	.pdf	application/pdf	4736	794	69	vector regression (SVR) owns the powerful capability to characterize problems via small sample, nonlinearity, high dimension and local minima. We found that the ν-SVR model optimized by genetic algorithms could be a Key words: Support vector regression, genetic algorithm, logistic model, prediction of biomass. studied optimal ν in SVR for different noise models using Parameters determination of the ν-SVR model SVR parameter settings, such as genetic algorithm (GA) find the optimal values of ν-SVR parameters. Where, ip denotes the predicted value in SVR model, optimal parameter of ν-SVR, the predicting model for the value modeling by GA-ν-SVR is shown in Figure 4. Performance comparison of SVR and logistic model GA-ν-SVR model was better than that of the logistic Comparison of the predicted results from GA-ν-SVR and logistic models. Measured and estimated cell concentration by logistic model (○-measured value ▲predicted value by the ν-SVR model. in support vector regression for different noise models and parameter 	./cache/work_nxdy636kujcvvj3ogvkz3iw7ca.pdf	./txt/work_nxdy636kujcvvj3ogvkz3iw7ca.txt