## Advances in Large Margin ClassifiersAlexander J. Smola, Peter J. Bartlett, Dale Schuurmans, Bernhard Schölkopf MIT Press, 2000 - 412 pages
The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba. |

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### Contents

vi | 22 |

Roadmap | 31 |

Dynamic Alignment Kernels | 39 |

Natural Regularization from Generative Models | 51 |

Probabilities for SV Machines | 61 |

Maximal Margin Perceptron | 75 |

Large Margin Rank Boundaries for Ordinal Regression | 115 |

Generalized Support Vector Machines | 135 |

Towards a Strategy for Boosting Regressors | 247 |

Bounds on Error Expectation for SVM | 261 |

Adaptive Margin Support Vector Machines | 281 |

GACV for Support Vector Machines | 297 |

Mean Field and LeaveOneOut | 311 |

Computing the Bayes Kernel Classifier | 329 |

Margin Distribution and Soft Margin | 349 |

Support Vectors and Statistical Mechanics | 359 |

Linear Discriminant and Support Vector Classifiers | 147 |

Regularization Networks and Support Vector Machines | 171 |

Functional Gradient Techniques for Combining Hypotheses | 221 |

Entropy Numbers for Convex Combinations and MLPs | 369 |