Theory and Practice of Support Vector Machines Optimization

Shai Shalev-Shwartz; Nathan Srebo

doi:10.1002/9780470742044.ch2

Theory and Practice of Support Vector Machines Optimization

Shai Shalev-Shwartz^*, Nathan Srebo

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

2 Scopus citations

Original language	English
Title of host publication	Automatic Speech and Speaker Recognition
Subtitle of host publication	Large Margin and Kernel Methods
Publisher	John Wiley & Sons, Ltd
Pages	11-26
Number of pages	16
ISBN (Print)	9780470696835
DOIs	https://doi.org/10.1002/9780470742044.ch2
State	Published - 14 Jan 2009
Externally published	Yes

Keywords

Bias term incorporation
Binary classification and traditional SVM
Cost-sensitive multiclass categorization
Dual decomposition methods
Gradient-based methods and loss functions
SVM training and linear prediction models
Sequence prediction and cost-sensitive multi-class categorization
Stochastic gradient descent (SGD) approach and training SVMs
Support Vector Machines (SVMs) optimization
approximation error with low-norm linear predictor

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10.1002/9780470742044.ch2

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title = "Theory and Practice of Support Vector Machines Optimization",

keywords = "Bias term incorporation, Binary classification and traditional SVM, Cost-sensitive multiclass categorization, Dual decomposition methods, Gradient-based methods and loss functions, SVM training and linear prediction models, Sequence prediction and cost-sensitive multi-class categorization, Stochastic gradient descent (SGD) approach and training SVMs, Support Vector Machines (SVMs) optimization, approximation error with low-norm linear predictor",

author = "Shai Shalev-Shwartz and Nathan Srebo",

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AU - Srebo, Nathan

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KW - Binary classification and traditional SVM

KW - Cost-sensitive multiclass categorization

KW - Dual decomposition methods

KW - Gradient-based methods and loss functions

KW - SVM training and linear prediction models

KW - Sequence prediction and cost-sensitive multi-class categorization

KW - Stochastic gradient descent (SGD) approach and training SVMs

KW - Support Vector Machines (SVMs) optimization

KW - approximation error with low-norm linear predictor

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SN - 9780470696835

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BT - Automatic Speech and Speaker Recognition

PB - John Wiley & Sons, Ltd

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Theory and Practice of Support Vector Machines Optimization

Keywords

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