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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Dynamic Alignment Kernels
Natural Regularization from Generative Models
Probabilities for SV Machines
Maximal Margin Perceptron
Large Margin Rank Boundaries for Ordinal Regression
Generalized Support Vector Machines
Linear Discriminant and Support Vector Classifiers
Functional Gradient Techniques for Combining Hypotheses
Towards a Strategy for Boosting Regressors
Bounds on Error Expectation for SVM
Adaptive Margin Support Vector Machines
GACV for Support Vector Machines
Mean Field and LeaveOneOut
Computing the Bayes Kernel Classifier
Margin Distribution and Soft Margin

Regularization Networks and Support Vector Machines
Robust Ensemble Learning
Entropy Numbers for Convex Combinations and MLPs

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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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