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Efficient Kernel Methods For Large Scale Classification

Name: Efficient Kernel Methods For Large Scale Classification
Brand: LAP Lambert Academic Publishing
SKU: 9783846541463
Price: 282.12 RON
Availability: InStock

Scalable methods for training Support Vector Machines

Autor: Asharaf S

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Legare: Carte broșată

Editura: LAP Lambert Academic Publishing

Disponibilitate: În depozitul extern

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282.12 lei

Classification algorithms have been widely used in many application domains. Most of these domains d...

Informații despre carte

Autor

Asharaf S

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2011

Pagini

132

EAN

9783846541463

Enbook ID

07082715

Editura

LAP Lambert Academic Publishing

Greutate

192

Dimensiuni

150 x 220 x 7

Descriere completă

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.

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Efficient Kernel Methods For Large Scale Classification

Scalable methods for training Support Vector Machines

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Descriere completă

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