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BibTeX

Empirical Inference Book Advances in Large Margin Classifiers Smola, A., Bartlett, P., Schölkopf, B., Schuurmans, D. 422, Neural Information Processing, MIT Press, Cambridge, MA, USA, October 2000

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.

Web BibTeX

Empirical Inference Book Chapter An Introduction to Kernel-Based Learning Algorithms Müller, K., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B. In Handbook of Neural Network Signal Processing, 4, (Editors: Yu Hen Hu and Jang-Neng Hwang), CRC Press, 2000 (Published) BibTeX

Empirical Inference Conference Paper Choosing nu in support vector regression with different noise models — theory and experiments Chalimourda, A., Schölkopf, B., Smola, A. In International Joint Conference on Neural Networks, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000, Neural Computing: New Challenges and Perspectives for the New Millennium, IEEE, International Joint Conference on Neural Networks, 2000 BibTeX

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