@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.}
}
