Advances in Large-Margin Classifiers (Neural Information Processing series)
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Von Händler/Antiquariat, The Book Depository US [58762574], Gloucester, ., United Kingdom.
Brand New Book. 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.

Von Händler/Antiquariat, Ami Ventures Inc Books [87079209], Houston, TX, U.S.A.
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. Books.

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Von Händler/Antiquariat, Amazon.com.
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., Printed Access Code, Label: A Bradford Book, A Bradford Book, Produktgruppe: Book, Publiziert: 2000-09-29, Studio: A Bradford Book.

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Von Händler/Antiquariat, Academiabooks.
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., Hardcover, Ausgabe: 1st, Label: A Bradford Book, A Bradford Book, Produktgruppe: Book, Publiziert: 2000-10-02, Studio: A Bradford Book, Verkaufsrang: 3061459.

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Von Händler/Antiquariat, bordeau_books.
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., Hardcover, Ausgabe: 1st, Label: A Bradford Book, A Bradford Book, Produktgruppe: Book, Publiziert: 2000-10-02, Studio: A Bradford Book, Verkaufsrang: 3787708.

Von Händler/Antiquariat, LowKeyBooks [65875000], Sma, WA, U.S.A.
Item is in very good condition. If supplemental codes/CDs for textbooks are required please contact us prior to purchasing as they may be missing. Photos are stock pictures and not of the actual item. Books.

Von Händler/Antiquariat, HPB Inc. [65440519], Dallas, TX, U.S.A.
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Advances in Large-Margin Classifiers, 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.

Von Händler/Antiquariat, The Second Reader Bookshop [1557786], Buffalo, NY, U.S.A.
Very Good with light wear to dust jacket and no marks to text. ; Neural Information Processing; 10.29 X 8.44 X 1.24 inches; 422 pages.

Von Händler/Antiquariat, Alien Bindings [71306383], BALTIMORE, MD, U.S.A.
Brand New in Brand New dust jacket. Electronic delivery tracking will be issued free of charge. Books.