10 edition of **Advances in Large-Margin Classifiers (Neural Information Processing)** found in the catalog.

- 233 Want to read
- 15 Currently reading

Published
**October 2, 2000**
by The MIT Press
.

Written in English

- Algorithms & procedures,
- Neural Networks,
- Algorithms (Computer Programming),
- Computers,
- Computers - General Information,
- Computer Books: General,
- Networking - General,
- Computers / Neural Networks,
- Artificial Intelligence - General,
- Programming - General,
- Algorithms,
- Kernel functions,
- Machine Learning

**Edition Notes**

Contributions | Peter J. Bartlett (Editor), Bernhard Schölkopf (Editor), Dale Schuurmans (Editor), Alex J Smola (Editor) |

The Physical Object | |
---|---|

Format | Hardcover |

Number of Pages | 422 |

ID Numbers | |

Open Library | OL9324992M |

ISBN 10 | 0262194481 |

ISBN 10 | 9780262194488 |

This book shows how this idea applies to both the theoretical analysis and the design of 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. Large margin classifiers are computed to assign patterns to a class with high confidence. This strategy helps controlling the capacity of the learning device so good generalization is presumably achieved. Two recent examples of large margin classifiers are support vector learning machines (SVM) and boosting classifiers. In this paper we show Cited by: 4.

Advances in Large-Margin Classifiers Alexander J. Smola, Peter Bartlett, Bernhard Schölkopf, and Dale Schuurmans 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. He is coauthor of Learning with Kernels () and is a coeditor of Advances in Kernel Methods: Support Vector Learning (), Advances in Large-Margin Classifiers (), and Kernel Methods in Computational Biology (), all published by the MIT Press.4/5(3).

A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Books. A. J. Smola, P. L. Bartlett, B. Schoelkopf, D. Schuurmans (editors). Advances in Large Margin Classifiers. MIT Press, Cambridge, USA,

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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.

The concept of large margins is a unifying principle for the analysis of many different approaches Advances in Large-Margin Classifiers book the classification of data. Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany.

He is coauthor of Learning with Kernels () and is a coeditor of Advances in Kernel Methods: Support Vector Learning (), Advances in Large-Margin Classifiers (), and Kernel Methods in Computational Biology (), all published by the MIT : Hardcover.

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany.

He is coauthor of Learning with Kernels () and is a coeditor of Advances in Kernel Methods: Support Vector Learning (), Advances in Large-Margin Classifiers (), and Kernel Methods in Computational Biology (), all published by the MIT Press.

Advances in Large-Margin Classifiers Book Abstract: 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.

Advances in Large Margin Classifiers. Abstract. From the Publisher: 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 book provides an overview. Advances in Large-Margin Classifiers by Alexander J. Smola,available at Book Depository with free delivery worldwide.3/5(2). Get this from a library. Advances in large margin classifiers. [Alexander J Smola;] -- 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.

Advances in large margin classifiers. [Alexander J Smola;] The book provides an overview of recent developments in large margin classifiers, # Advances in neural information processing systems [i.e.

Neural information processing series]\/span>\n \u00A0\u00A0\u00A0\n schema. Advances in Large-Margin Classifiers (Hardback) ~ Doc / DA1PWGTDH9 Advances in Large-Margin Classifiers (Hardback) By - MIT Press Ltd, United States, Hardback.

Book Condition: New. New. x mm. Language: English. Brand New Book. The concept of large margins is a unifying principle for. 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 by: 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.

The concept of large margins Author: Alexander J. Smola. Request PDF | On Jan 1,Ralf Herbrich and others published Advances in Large Margin Classifiers | Find, read and cite all the research you need on ResearchGate. Advances in Large-Margin Classifiers Edited by Peter J. Bartlett, Bernhard Schölkopf, Dale Schuurmans, and Alexander J.

Smola, MIT Press, 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. Mathematics Behind Large Margin Classification.

And this is why this machine ends up with enlarge margin classifiers because itss trying to maximize the norm of these P1 which is the distance from the training examples to the decision boundary. Finally, we did this whole derivation using this simplification that the parameter theta 0 must.

26 cm Ill., graph. Darst Includes bibliographical references (p. []) and index Contains papers based on talks presented at the two-day workshop at the annual Neural Information Processing Systems (NIPS) conference, Breckenridge, Colorado, Decemberwith articles describing results obtained since the workshop.

Schölkopf is coauthor of Learning with Kernels (MIT Press, ) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (), Advances in Large-Margin Classifiers (), and Kernel Methods in Computational Biology (), all published by The MIT Press.

DH Adaptive Computation and Machine Learning series 0. He is coauthor of Learning with Kernels () and is a coeditor of Advances in Kernel Methods: Support Vector Learning (), Advances in Large-Margin Classifiers (), and Kernel Methods in Computational Biology (), all published by the MIT Press.4/5(1).

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.

Books. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, Eds., Advances in Large Margin Classifiers, Neural Information Processing Series, Cambridge, MA. I contributed specifically observe this download advances in large margin classifiers neural.

suggested on July 8, by J. 0 out of 5 studies for download advances in large margin O you would let to be the homes, you wo wholly enter it. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): The output of a classifier should be a calibrated posterior probability to enable post-processing.

Standard SVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized maximum likelihood score.Support vector machine definition of margin.

See support vector machines and maximum-margin hyperplane for details. Margin for boosting algorithms. The margin for an iterative boosting algorithm given a set of examples with two classes can be defined as follows. The classifier is given an example pair (,) where ∈ is a domain space and ∈ = {−, +} is the label of the example.Advances in Large Margin Classifiers, chapter Large margin rank boundaries for ordinal regression () by R Herbrich, T Graepel, K Obermayer Add To MetaCart.