@article { ISI:000224609100004,
	title = {SVM classifier incorporating simultaneous noise reduction and feature selection: illustrative case examples},
	journal = {Pattern Recognition},
	volume = {38},
	number = {1},
	year = {2005},
	month = {JAN},
	pages = {41-49},
	publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
	type = {Article},
	address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
	abstract = {<p>A hybrid technique involving symbolization of data to remove noise and use of conditional entropy minima to extract relevant and non-redundant features is proposed in conjunction with support vector machines to obtain more robust classification algorithm. The technique tested on three data sets shows improvements in classification efficiencies. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.</p>},
	keywords = {classification, conditional entropy, SVM, symbolization},
	issn = {0031-3203},
	doi = {10.1016/j.patcog.2004.06.002},
	author = {Kumar, R. 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.}
}
