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Synopsis
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.
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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About the Author
Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.
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- PublisherBradford Books
- Publication date2000
- ISBN 10 0262194481
- ISBN 13 9780262194488
- BindingHardcover
- Edition number1
- Number of pages422
- EditorSmola Alexander J, Bartlett Nottinghamshire Healthcare Nhs Trust Chair In Mental Health Law Peter, Schölkopf Director Of The Max Planck Institute For Intelligent In Tübingen Germany Professor For Machine Lea Bernhard, Schuurmans Dale
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Advances in Large-Margin Classifiers (Neural Information Processing)
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ISBN 10: 0262194481
ISBN 13: 9780262194488
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Hardcover. Condition: New. First Edition. 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.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. Seller Inventory # DADAX0262194481
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Advances in Large-Margin Classifiers
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Advances in Large-Margin Classifiers
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