Efficient Kernel Methods For Large Scale Classification: Scalable methods for training Support Vector Machines - Couverture souple

S, Asharaf

 
9783846541463: Efficient Kernel Methods For Large Scale Classification: Scalable methods for training Support Vector Machines
Couverture souple
ISBN 10 : 384654146X ISBN 13 : 9783846541463
Editeur : LAP LAMBERT Academic Publishing, 2011

Synopsis

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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Présentation de l'éditeur

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.

Biographie de l'auteur

Asharaf S is a faculty member in the IT&Systems area in IIM, Kozhikode, India. He received his PhD and Master of Engineering degrees from Indian Institute of Science, Bangalore. Prior to joining IIMK, he has been with America Online as a Research Scientist. He is a recipient of IBM Outstanding PhD student award.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2011
Langue
anglais
ISBN 10 
384654146X
ISBN 13 
9783846541463
Reliure
Broché
Nombre de pages
132
Coordonnées du fabricant
non disponible
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