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Erschienen in:
Augmented-SVM for Gradient Observations with Application to Learning Multiple-Attractor Dynamics Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

2. Multi-Class Support Vector Machine

verfasst von : Zhe Wang, Xiangyang Xue

Erschienen in: Support Vector Machines Applications

Verlag: Springer International Publishing

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Abstract

Support vector machine (SVM) was initially designed for binary classification. To extend SVM to the multi-class scenario, a number of classification models were proposed such as the one by Crammer and Singer (J Mach Learn Res 2:265–292, 2001). However, the number of variables in Crammer and Singer’s dual problem is the product of the number of samples (l) by the number of classes (k), which produces a large computational complexity. This chapter sorts the existing classical techniques for multi-class SVM into the indirect and direct ones and further gives the comparison for them in terms of theory and experiments. Especially, this chapter exhibits a new Simplified Multi-class SVM (SimMSVM) that reduces the size of the resulting dual problem from l × k to l by introducing a relaxed classification error bound. The experimental discussion demonstrates that the SimMSVM approach can greatly speed up the training process, while maintaining a competitive classification accuracy.

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Vorheriges Kapitel Augmented-SVM for Gradient Observations with Application to Learning Multiple-Attractor Dynamics
Nächstes Kapitel Novel Inductive and Transductive Transfer Learning Approaches Based on Support Vector Learning
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Metadaten
Titel
Multi-Class Support Vector Machine
verfasst von
Zhe Wang
Xiangyang Xue
Copyright-Jahr
2014
Buch
Support Vector Machines Applications

Print ISBN: 978-3-319-02299-4

Electronic ISBN: 978-3-319-02300-7

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-02300-7_2

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