Parametric and Nonparametric Classifiers Applied to the Supervised Classification of Proteomic Data

Author	Search for: Barton, Alan
Format	Text, Technical Report
Subject	pattern recognition; bayes; maximum likelihood; mass spectrometry; proteins; bayésien; maximum de vraisemblance; spectrométrie de masse; protéines
Abstract	An investigation of the properties of 3 classifiers was performed; 2 parametric and 1 non-parametric. The former classifiers were the linear and quadratic discriminant functions for which both bayesian and maximum likelihood learning (estimation) were computed; while the latter was the k-nearest neighbour classifier (k = 3 in this study). The convergence of the estimates was reported along with the classification accuracies of all classifiers. It was seen that the particular class structure studied was quite complex, leading to difficulties in predicting all samples correctly.
Publication date	2006
Language	English
NRC number	NRCC 48805
NPARC number	8914071
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Record identifier	6d933710-8cb6-4869-abd6-87cca6881dc0
Record created	2009-04-22
Record modified	2020-03-06