Efficient Kernel Methods For Large Scale Classification

Language: English

Published by LAP LAMBERT Academic Publishing Okt 2011, 2011

384654146X / 9783846541463

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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

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This item is printed on demand - it takes 3-4 days longer - Neuware -Classification algorithms have been widely used in many application domains. Most of these domains deal with massive collection of data and hence demand classification algorithms that scale well with the size of the data sets involved. A classification algorithm is said to be scalable if there is no significant increase in time and space requirements for the algorithm (without compromising the generalization performance) when dealing with an increase in the training set size. Support Vector Machine (SVM) is one of the most celebrated kernel based classification methods used in Machine Learning. An SVM capable of handling large scale classification problems will definitely be an ideal candidate in many real world applications. The training process involved in SVM classifier is usually formulated as a Quadratic Programing (QP) problem. The existing solution strategies for this problem have an associated time and space complexity that is (at least) quadratic in the number of training points. It makes SVM training very expensive. This thesis addresses the scalability of the training algorithms involved in SVM to make it feasible with large training data sets. 132 pp. Englisch.

Seller Inventory # 9783846541463

Title
Efficient Kernel Methods For Large Scale Classification
Author
Asharaf S
Publisher
LAP LAMBERT Academic Publishing Okt 2011
Publication year
2011
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
384654146X
ISBN 13
9783846541463
Item weight
215 grams
Dimensions
220x150x8 mm

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Germany

5-star seller

AbeBooks seller since January 11, 2012

Shipping rates from Germany to U.S.A.

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BuchWeltWeit Ludwig Meier e.K.

Germany