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

AuthorSearch for:
TypeTechnical Report
Subjectpattern recognition; bayes; maximum likelihood; mass spectrometry; proteins
AbstractAn 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
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48805
NPARC number8914071
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Record identifier6d933710-8cb6-4869-abd6-87cca6881dc0
Record created2009-04-22
Record modified2016-05-09
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