On Combining Classification Algorithms (Paperback)


The ability of a chosen classification algorithm to induce a good generalization depends on how appropriate its representation language used to express generalizations of the examples is for the given task. Since different learning algorithms employ different knowledge representations and search heuristics, different search spaces are explored and diverse results are obtained. The problem of finding the appropriate model for a given task is an active research area. In this dissertation, instead of looking for methods that fit the data using a single representation language, we present a family of algorithms, under the generic name of {em Cascade Generalization}, whose search spaces contains models that use different representation languages.

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Product Description

The ability of a chosen classification algorithm to induce a good generalization depends on how appropriate its representation language used to express generalizations of the examples is for the given task. Since different learning algorithms employ different knowledge representations and search heuristics, different search spaces are explored and diverse results are obtained. The problem of finding the appropriate model for a given task is an active research area. In this dissertation, instead of looking for methods that fit the data using a single representation language, we present a family of algorithms, under the generic name of {em Cascade Generalization}, whose search spaces contains models that use different representation languages.

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Product Details

General

Imprint

VDM Verlag

Country of origin

Germany

Release date

June 2009

Availability

Expected to ship within 10 - 15 working days

First published

June 2009

Authors

Dimensions

229 x 152 x 12mm (L x W x T)

Format

Paperback - Trade

Pages

200

ISBN-13

978-3-639-16746-7

Barcode

9783639167467

Categories

LSN

3-639-16746-5



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