Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods

Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Platt, J. C In Advances in large margin classifiers, 5. MIT Press, Cambridge, Massachusetts, 2000.

Paper abstract bibtex

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.

@InCollection{platt00probabilistic,
  author    = {Platt, John C},
  title     = {Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods},
  booktitle = {Advances in large margin classifiers},
  publisher = {MIT Press},
  year      = {2000},
  chapter   = {5},
  address   = {Cambridge, Massachusetts},
  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. 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.},
  comment   = {Advances in Large-Margin Classifiers appeared September 2000},
  keywords  = {SVM; Support Vector Machine; Platt probabilities; posterior probabilities;},
  opteditor = {A. Smola and P. Bartlett and B. Sch\"olkopf and D. Schuurmans},
  owner     = {Sebastian},
  timestamp = {2014.07.02},
  url       = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639&amp;g},
}

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