@article { ISI:000231744300010,
	title = {Regression models using pattern search assisted least square support vector machines},
	journal = {Chemical Engineering Research and Design},
	volume = {83},
	number = {A8},
	year = {2005},
	month = {AUG},
	pages = {1030-1037},
	publisher = {INST CHEMICAL ENGINEERS},
	type = {Article},
	address = {165-189 RAILWAY TERRACE, DAVIS BLDG, RUGBY CV21 3HQ, ENGLAND},
	abstract = {<p>Least Square Support Vector Machines (LS-SVM), a new machine-learning tool has been employed for developing data driven models of non-linear processes. The method is firmly rooted in the statistical learning theory and transforms the input data to a higher dimensional feature space where the use of appropriate kernel functions avoid computational difficulty. Further, a pattern search algorithm, which explores multiple directions and utilizes coordinate search with fixed step size, is employed for selecting optimal LS-SVM model that produces a minimum possible prediction error. To show the efficacy and efficiency of the fully automated pattern search assisted LS-SVM methodology, we have tested it on several benchmark examples. The study suggests that proposed paradigm can be a useful and viable tool in building data driven models of non-linear processes.</p>},
	keywords = {equality constraints, LS-SVM, model selection, Optimization, pattern search},
	issn = {0263-8762},
	doi = {10.1205/cherd.03144},
	author = {Patil, N. S. and Shelokar, P. S. and Jayaraman, Valadi K. and Kulkarni, B. D.}
}
