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11 - Support vector learning models for outlier detection
- from Part V - Support vector machines and variants
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- Kernel Methods and Machine Learning
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- 17 April 2014, pp 380-394
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15 - Kernel methods for estimation, prediction, and system identification
- from Part VII - Kernel methods and statistical estimation theory
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- Kernel Methods and Machine Learning
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- 17 April 2014, pp 494-536
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5 - Unsupervised learning for cluster discovery
- from Part III - Unsupervised learning models for cluster analysis
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- 17 April 2014, pp 141-177
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7 - Kernel-based regression and regularization analysis
- from Part IV - Kernel ridge regressors and variants
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- 17 April 2014, pp 221-247
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9 - Kernel ridge regression for super vised classification
- from Part IV - Kernel ridge regressors and variants
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- 17 April 2014, pp 282-340
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1 - Fundamentals of kernel-based machine learning
- from Part I - Machine learning and kernel vector spaces
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- 17 April 2014, pp 3-43
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Part VI - Kernel methods for green machine learning technologies
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- 17 April 2014, pp 419-420
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Part III - Unsupervised learning models for cluster analysis
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- 17 April 2014, pp 139-140
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Appendix A - Validation and testing of learning models
- from Part VIII - Appendices
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- 17 April 2014, pp 539-548
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12 - Ridge-SVM learning models
- from Part V - Support vector machines and variants
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- 17 April 2014, pp 395-418
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13 - Efficient kernel methods for learning and classification
- from Part VI - Kernel methods for green machine learning technologies
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- 17 April 2014, pp 421-456
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Part V - Support vector machines and variants
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- 17 April 2014, pp 341-342
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Frontmatter
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- 17 April 2014, pp i-iv
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8 - Linear regression and discriminant analysis for supervised classification
- from Part IV - Kernel ridge regressors and variants
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- 17 April 2014, pp 248-281
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Part IV - Kernel ridge regressors and variants
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- 17 April 2014, pp 219-220
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2 - Kernel-induced vector spaces
- from Part I - Machine learning and kernel vector spaces
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- 17 April 2014, pp 44-76
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4 - Feature selection
- from Part II - Dimension-reduction: PCA/KPCA and feature selection
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- 17 April 2014, pp 118-138
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Preface
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- 17 April 2014, pp xvii-xxiv
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Part I - Machine learning and kernel vector spaces
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- 17 April 2014, pp 1-2
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6 - Kernel methods for cluster analysis
- from Part III - Unsupervised learning models for cluster analysis
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- 17 April 2014, pp 178-218
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