[PDF.32fj] Advances in Large-Margin Classifiers (Neural Information Processing)

Advances in Large-Margin Classifiers (Neural Information Processing)

[PDF.lc54] Advances in Large-Margin Classifiers (Neural Information Processing)

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 | #2411873 in Books |  2000-10-02 | Original language:English | PDF # 1 |  10.00 x1.20 x8.00l,2.70 | File type: PDF | 422 pages

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||About the Author|Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent

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 applie...

You easily download any file type for your device.Advances in Large-Margin Classifiers (Neural Information Processing)   |  From A Bradford Book. I really enjoyed this book and have already told so many people about it!

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