Header logo is

3 results (View BibTeX file of all listed publications)

2017

no image
Elements of Causal Inference - Foundations and Learning Algorithms

Peters, J., Janzing, D., Schölkopf, B.

Adaptive Computation and Machine Learning Series, The MIT Press, Cambridge, MA, USA, 2017 (book)

ei

PDF [BibTex]

2017

ei Peters, J., Janzing, D., Schölkopf, B. Elements of Causal Inference - Foundations and Learning Algorithms Adaptive Computation and Machine Learning Series, The MIT Press, Cambridge, MA, USA, 2017 (book)

PDF [BibTex]

no image
New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481)

Gretton, A., Hennig, P., Rasmussen, C., Schölkopf, B.

Dagstuhl Reports, 6(11):142-167, 2017 (book)

ei pn

DOI [BibTex]

ei pn Gretton, A., Hennig, P., Rasmussen, C., Schölkopf, B. New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481) Dagstuhl Reports, 6(11):142-167, 2017 (book)

DOI [BibTex]

2000

no image
Advances in Large Margin Classifiers

Smola, A., Bartlett, P., Schölkopf, B., Schuurmans, D.

pages: 422, Neural Information Processing, MIT Press, Cambridge, MA, USA, October 2000 (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. 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.

ei

Web [BibTex]

2000

ei Smola, A., Bartlett, P., Schölkopf, B., Schuurmans, D. Advances in Large Margin Classifiers pages: 422, Neural Information Processing, MIT Press, Cambridge, MA, USA, October 2000 (book)

Web [BibTex]