@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.}
}
@article { ISI:000266525900010,
	title = {Review on lazy learning regressors and their applications in QSAR},
	journal = {Combinatorial Chemistry \& High Throughput Screening},
	volume = {12},
	number = {4},
	year = {2009},
	month = {MAY},
	pages = {440-450},
	publisher = {BENTHAM SCIENCE PUBL LTD},
	address = {EXECUTIVE STE Y26, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES},
	abstract = {<p>Building accurate quantitative structure-activity relationships (QSAR) is important in drug design, environmental modeling, toxicology, and chemical property prediction. QSAR methods can be utilized to solve mainly two types of problems viz., pattern recognition, (or classification) where output is discrete (i.e. class information), e. g., active or non-active molecule, binding or non-binding molecule etc., and function approximation, (i.e. regression) where the output is continuous (e.g., actual activity prediction). The present review deals with the second type of problem (regression) with specific attention to one of the most effective machine learning procedures, viz. lazy learning. The methodologies of the algorithm along with the relevant technical information are discussed in detail. We also present three real life case studies to briefly outline the typical characteristics of the modeling formalism.</p>},
	keywords = {classification, lazy learning, machine learning, Quantitative structure activity relationship (QSAR), regression},
	issn = {1386-2073},
	author = {Kulkarni, Abhijit J. and Jayaraman, Valadi K. and Kulkarni, Bhaskar D.}
}
