Book contents
- Frontmatter
- Contents
- Foreword
- Preface
- 1 The framework of learning
- 2 Basic hypothesis spaces
- 3 Estimating the sample error
- 4 Polynomial decay of the approximation error
- 5 Estimating covering numbers
- 6 Logarithmic decay of the approximation error
- 7 On the bias–variance problem
- 8 Least squares regularization
- 9 Support vector machines for classification 157
- 10 General regularized classifiers
- References
- Index
10 - General regularized classifiers
Published online by Cambridge University Press: 05 March 2010
Book contents
- Frontmatter
- Contents
- Foreword
- Preface
- 1 The framework of learning
- 2 Basic hypothesis spaces
- 3 Estimating the sample error
- 4 Polynomial decay of the approximation error
- 5 Estimating covering numbers
- 6 Logarithmic decay of the approximation error
- 7 On the bias–variance problem
- 8 Least squares regularization
- 9 Support vector machines for classification 157
- 10 General regularized classifiers
- References
- Index
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- Chapter
- Information
- Learning TheoryAn Approximation Theory Viewpoint, pp. 187 - 213Publisher: Cambridge University PressPrint publication year: 2007