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Lingua: Inglese
Editore: Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
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Hardcover. Condizione: 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!
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Lingua: Inglese
Editore: Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE, 1997
ISBN 10: 3540626859 ISBN 13: 9783540626855
Da: Rarewaves.com USA, London, LONDO, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: New. 1997 ed.
Da: Ria Christie Collections, Uxbridge, Regno Unito
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ISBN 10: 1107057132 ISBN 13: 9781107057135
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Editore: Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
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ISBN 10: 1107057132 ISBN 13: 9781107057135
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. pp. 424 1st edition.
Lingua: Inglese
Editore: Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Da: Majestic Books, Hounslow, Regno Unito
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Paperback or Softback. Condizione: New. Computational Learning Theory: Third European Conference, Eurocolt '97, Jerusalem, Israel, March 17 - 19, 1997, Proceedings. Book.
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EUR 66,50
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ISBN 10: 1107057132 ISBN 13: 9781107057135
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Aggiungi al carrelloHardback. Condizione: New. Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
Lingua: Inglese
Editore: Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Da: Biblios, Frankfurt am main, HESSE, Germania
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Aggiungi al carrelloCondizione: New. pp. 424.
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ISBN 10: 1107057132 ISBN 13: 9781107057135
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Aggiungi al carrelloHardback. Condizione: New. Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
Lingua: Inglese
Editore: Cambridge University Press, 2016
ISBN 10: 1107057132 ISBN 13: 9781107057135
Da: moluna, Greven, Germania
EUR 66,41
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Aggiungi al carrelloGebunden. Condizione: New. Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the hows an.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 53,49
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed proceedings of the Third European Conference on Computational Learning Theory, EuroCOLT'97, held in Jerusalem, Israel, in March 1997.The book presents 25 revised full papers carefully selected from a total of 36 high-quality submissions. The volume spans the whole spectrum of computational learning theory, with a certain emphasis on mathematical models of machine learning. Among the topics addressed are machine learning, neural nets, statistics, inductive inference, computational complexity, information theory, and theoretical physics.
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Aggiungi al carrelloHardcover. Condizione: Brand New. 397 pages. 10.00x7.00x1.00 inches. In Stock.
Da: AHA-BUCH GmbH, Einbeck, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the eld, placing each of these topics in the general context of the eld. Formal models of automated learning re ect various facets of the wide range of activities that can be viewed as learning. A rst dichotomy is between viewing learning as an inde nite process and viewing it as a nite activity with a de ned termination. Inductive Inference models focus on inde nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Computational Learning Theory | Third European Conference, EuroCOLT '97, Jerusalem, Israel, March 17 - 19, 1997, Proceedings | Shai Ben-David | Taschenbuch | cccxlviii | Englisch | 1997 | Springer | EAN 9783540626855 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Cambridge University Press, GB, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Da: Rarewaves USA United, OSWEGO, IL, U.S.A.
EUR 91,01
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Aggiungi al carrelloHardback. Condizione: New. Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
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EUR 61,94
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Aggiungi al carrelloPaperback. Condizione: New. 1997th.
Lingua: Inglese
Editore: Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 110,62
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Aggiungi al carrelloHardcover. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 134,41
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Aggiungi al carrelloPaperback. Condizione: Like New. Like New. book.