Idioma: Inglés
Publicado por Jewish Lights 2013-04-01, 2013
ISBN 10: 1683361466 ISBN 13: 9781683361466
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Idioma: Inglés
Publicado por Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
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Idioma: Inglés
Publicado por Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
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Idioma: Inglés
Publicado por Cambridge University Press CUP, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 76,30
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Añadir al carritoCondición: New. pp. 424 1st edition.
Idioma: Inglés
Publicado por Cambridge University Press 2014-05-19, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Librería: Chiron Media, Wallingford, Reino Unido
EUR 64,75
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Añadir al carritoHardcover. Condición: New.
Idioma: Inglés
Publicado por Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
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Añadir al carritoCondición: New. pp. 424 47 Illus.
Idioma: Inglés
Publicado por Cambridge University Press, GB, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
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Añadir al carritoHardback. Condición: 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.
Idioma: Inglés
Publicado por Cambridge University Press, GB, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Librería: Rarewaves USA, OSWEGO, IL, Estados Unidos de America
EUR 83,12
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Añadir al carritoHardback. Condición: 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.
Idioma: Inglés
Publicado por Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 67,69
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Idioma: Inglés
Publicado por Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 76,98
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Añadir al carritoCondición: New. pp. 424.
Idioma: Inglés
Publicado por Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 74,79
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Librería: Turgid Tomes, Nashville, TN, Estados Unidos de America
EUR 89,18
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Añadir al carritopaperback. Condición: Very Good. Academic Press, 2024. Trade paperback in VG condition; a nice clean copy.
Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoHardcover. Condición: Brand New. 397 pages. 10.00x7.00x1.00 inches. In Stock.
Idioma: Inglés
Publicado por Springer Berlin Heidelberg, Springer Berlin Heidelberg Sep 2004, 2004
ISBN 10: 3540233563 ISBN 13: 9783540233565
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 53,49
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware -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. 528 pp. Englisch.
Idioma: Inglés
Publicado por Cambridge University Press, 2016
ISBN 10: 1107057132 ISBN 13: 9781107057135
Librería: moluna, Greven, Alemania
EUR 66,41
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Añadir al carritoGebunden. Condición: 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.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 53,49
Cantidad disponible: 1 disponibles
Añadir al carritoTaschenbuch. Condición: 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.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 53,49
Cantidad disponible: 1 disponibles
Añadir al carritoTaschenbuch. Condición: 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.
Idioma: Inglés
Publicado por Cambridge University Press, GB, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de America
EUR 84,82
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Añadir al carritoHardback. Condición: 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.
Idioma: Inglés
Publicado por Cambridge University Press, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Librería: Mispah books, Redhill, SURRE, Reino Unido
EUR 109,98
Cantidad disponible: 1 disponibles
Añadir al carritoHardcover. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Idioma: Inglés
Publicado por Cambridge University Press, GB, 2014
ISBN 10: 1107057132 ISBN 13: 9781107057135
Librería: Rarewaves.com UK, London, Reino Unido
EUR 77,24
Cantidad disponible: Más de 20 disponibles
Añadir al carritoHardback. Condición: 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.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 143,08
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Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: Mispah books, Redhill, SURRE, Reino Unido
EUR 133,63
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Añadir al carritoPaperback. Condición: Like New. Like New. book.
Librería: Majestic Books, Hounslow, Reino Unido
EUR 156,56
Cantidad disponible: 3 disponibles
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 165,46
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