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ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer] Hardcover This is a fine, as new, hardcover second edition copy, no DJ, brown spine. 314 pages with index. [Raleigh, NC, U.S.A.] [Publication Year: 1999]
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer] Hardcover Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages. [Berlin, Germany] [Publication Year: 1999]
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer] Hardcover Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages. [Berlin, Germany] [Publication Year: 1999]
Springer 1999 2nd 2000 ed. hardcover Good Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer] Hardcover Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! [Dallas, TX, U.S.A.] [Publication Year: 1999]
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer] Hardcover Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 1.15 [Dallas, TX, U.S.A.] [Publication Year: 1999]
Springer New York, Date: 1999. Hardcover. Good. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.Dust jacket quality is not guaranteed. 1999. Springer New York ISBN 0387987800 9780387987804 [US]
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer] Hardcover 100% Customer Satisfaction Guaranteed ! The book shows some signs of wear from use but is a good readable copy. Cover in excellent condition. Binding tight. Pages in great shape, no tears. Not contain access codes, cd, DVD. [Suffolk, United Kingdom] [Publication Year: 1999]
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer] Hardcover Book is in Used-Good condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain limited notes and highlighting. [Hawthorne, CA, U.S.A.] [Publication Year: 1999]
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer] Hardcover Buy with confidence! Book is in acceptable condition with wear to the pages, binding, and some marks within [Amherst, NY, U.S.A.] [Publication Year: 1999]
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer New York] Hardcover The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. Written in readable and concise style and devoted to key learning problems, the book is intended for statisticians, mathematicia. [Greven, Germany] [Publication Year: 1999]
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer New York] Hardcover The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. Written in readable and concise style and devoted to key learning problems, the book is intended for statisticians, mathematicia. [Greven, Germany] [Publication Year: 1999]
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer] Hardcover New Book. Shipped from UK. Established seller since 2000. [Fairford, GLOS, United Kingdom] [Publication Year: 1999]
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer] Hardcover New Book. Shipped from UK. Established seller since 2000. [Wood Dale, IL, U.S.A.] [Publication Year: 1999]
Springer 11/19/1999 12: 00: 00 AM 2nd ed. 2000 Hardcover PLEASE NOTE, WE DO NOT SHIP TO DENMARK. New Book from multilingual publisher. Shipped from UK within 4 to 14 days. Please check language within the description.
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer-Verlag Gmbh Nov 1999] Hardcover Neuware - The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: \* the setting of learning problems based on the model of minimizing the risk functional from empirical data \* a comprehensive analysis of the empirical risk minimization principle including necessary and sufficient conditions for its consistency \* non-asymptotic bounds for the risk achieved using the empirical risk minimization principle \* principles for controlling the generalization ability of learning machines using small sample sizes based on these bounds \* the Support Vector methods that control the generalization ability when estimating function using small sample size. The second edition of the book contains three new chapters devoted to further development of the learning theory and SVM techniques. These include: \* the theory of direct method of learning based on solving multidimensional integral equations for density, conditional probability, and conditional density estimation \* a new inductive principle of learning. Written in a readable and conc ...
Springer 11/19/1999 12: 00: 00 AM 2nd ed. 2000 Hardcover PLEASE NOTE, WE DO NOT SHIP TO DENMARK. New Book from multilingual publisher. Shipped from UK within 4 to 14 days. Please check language within the description.
Springer 11/19/1999 12: 00: 00 AM 2nd ed. 2000 Hardcover New Book from multilingual publisher. Shipped from UK within 4 to 14 days. Please check language within the description.
New York, NY Springer 1999 2nd 2000 ed. Hard cover New. Sewn binding. Cloth over boards. 314 p. Contains: Unspecified. Information Science and Statistics.
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer-Verlag Gmbh Nov 1999] Hardcover Neuware -The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: \* the setting of learning problems based on the model of minimizing the risk functional from empirical data \* a comprehensive analysis of the empirical risk minimization principle including necessary and sufficient conditions for its consistency \* non-asymptotic bounds for the risk achieved using the empirical risk minimization principle \* principles for controlling the generalization ability of learning machines using small sample sizes based on these bounds \* the Support Vector methods that control the generalization ability when estimating function using small sample size. The second edition of the book contains three new chapters devoted to further development of the learning theory and SVM techniques. These include: \* the theory of direct method of learning based on solving multidimensional integral equations for density, conditional probability, and conditional density estimation \* a new inductive principle of learning. Written in a readable and conci ...
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer-Verlag Gmbh Nov 1999] Hardcover Neuware -The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: \* the setting of learning problems based on the model of minimizing the risk functional from empirical data \* a comprehensive analysis of the empirical risk minimization principle including necessary and sufficient conditions for its consistency \* non-asymptotic bounds for the risk achieved using the empirical risk minimization principle \* principles for controlling the generalization ability of learning machines using small sample sizes based on these bounds \* the Support Vector methods that control the generalization ability when estimating function using small sample size. The second edition of the book contains three new chapters devoted to further development of the learning theory and SVM techniques. These include: \* the theory of direct method of learning based on solving multidimensional integral equations for density, conditional probability, and conditional density estimation \* a new inductive principle of learning. Written in a readable and conci ...
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer-Verlag Gmbh Nov 1999] Hardcover Neuware -The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists. [Zwiesel, Germany] [Publication Year: 1999]
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer-Verlag Gmbh Nov 1999] Hardcover Neuware - The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: \* the setting of learning problems based on the model of minimizing the risk functional from empirical data \* a comprehensive analysis of the empirical risk minimization principle including necessary and sufficient conditions for its consistency \* non-asymptotic bounds for the risk achieved using the empirical risk minimization principle \* principles for controlling the generalization ability of learning machines using small sample sizes based on these bounds \* the Support Vector methods that control the generalization ability when estimating function using small sample size. The second edition of the book contains three new chapters devoted to further development of the learning theory and SVM techniques. These include: \* the theory of direct method of learning based on solving multidimensional integral equations for density, conditional probability, and conditional density estimation \* a new inductive principle of learning. Written in a readable and conc ...
Hard Cover. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; The The Nature of Statistical Learning Theory. ISBN 0387987800 9780387987804 [GB]
ISBN10: 0387987800, ISBN13: 9780387987804, [publisher: Springer] Hardcover New. Fast Shipping and good customer service [Fayetteville, TX, U.S.A.] [Publication Year: 1999]
New York, NY Springer 1999 2nd 2000 ed. Hard cover New. Sewn binding. Cloth over boards. 314 p. Contains: Unspecified. Information Science and Statistics.
DISCLOSURE:
When you click on links to various merchants on this site and make a purchase, this can result in this site earning a commission at no extra cost to you. Affiliate programs and affiliations include, but are not limited to, the eBay Partner Network, Amazon and Alibris.
