Combining Models in Classification and Pattern Pecognition
Publish place: 05th Iranian Statistics Conference
Publish Year: 1379
Type: Conference paper
Language: English
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Document National Code:
ISC05_013
Index date: 4 January 2010
Combining Models in Classification and Pattern Pecognition abstract
Data-based procedures are proposed for combining a number of individual classifiers in order to construct more effective classification rules. The resulting combined classifiers turn out to be almost surely superior to each individual classifier, under appropriate regularity conditions. Here, superiority means lower asymptotic misclassification error rate. Both the mechanics and the asymptotic validity of the proposed procedures are discussed.
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Combining Models in Classification and Pattern Pecognition authors
Majid Mojirsheibani
This research was supported in part by a grant from NSERC Canada. Scholl of Mathematics & Statistics, Carleton University, Ottawa, Ontario, KIS ۵B۶ Canada.