Advances in Large Margin Classifiers

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Alexander J. Smola
MIT Press, 2000 - Computers - 412 pages
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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

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
Linear Discriminant and Support Vector Classifiers
147
Functional Gradient Techniques for Combining Hypotheses
221
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

Regularization Networks and Support Vector Machines
171
Robust Ensemble Learning
207
Entropy Numbers for Convex Combinations and MLPs
369
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Page 391 - PS Bradley and OL Mangasarian. Feature selection via concave minimization and support vector machines. In J. Shavlik, editor, Machine Learning Proceedings of the Fifteenth International Conference (ICML '98), pages 82-90, San Francisco, California, 1998.
Page 406 - In B. Scholkopf, CJC Burges, and AJ Smola, editors, Advances in Kernel Methods — Support Vector Learning, pages 69-88, Cambridge, MA, 1999b.
Page 391 - C. Burges, V. Vapnik, and T. Vetter, "Comparison of View-based Object Recognition Algorithms Using Realistic 3D Models," Artificial Neural Networks - ICANN'96, Springer Lecture Notes in Computer Science, vol.
Page 391 - CJC Burges and B. Scholkopf. Improving the accuracy and speed of support vector learning machines.
Page 392 - I. Daubechies, Ten Lectures on Wavelets, SIAM, Philadelphia, PA, 1992. Notes from the 1990 CBMS-NSF Conference on Wavelets and Applications at Lowell, MA.
Page 395 - Learning, 1996. 6. AJ Grove, and D. Schuurmans. Boosting in the limit: Maximizing the margin of learned ensembles.
Page 390 - Mar. 1991. [2] M. Bertero, T. Poggio, and V. Torre, "Ill-posed problems in early vision,

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About the author (2000)

Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader atNational ICT Australia/Australian National University, Canberra.

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