Mehryar (65 risultati)

Hardcover. Condizione: Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Hardcover. Condizione: Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Condizione: acceptable.

Hardback. Condizione: Good. A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.…

Condizione: good. Has a sturdy binding with some shelf wear. May have some markings or highlighting. Used copies may not include access codes or Cd's. Slight bending may be present.

Hardback. Condizione: Good. A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.…

Condizione: Very Good. Text block firm and clean, binding unblemished, boards straight, without highlights or underlining. Very clean, nearly like new. Without any discs, access codes or extra items. Supporting Bay Area Friends of the Library since 2010. Well packaged and promptly shipped.

Hardcover. Condizione: Near Fine. No Jacket. xii,412. No jacket, as issued. Bottom corners are bumped, tail of the spine pressed. Spine slightly cocked. Otherwise pristine. If you order this from outside the United States, we will almost certainly request an additional payment to help cover the postage. Please feel welcome to message us with any questions, or a request for photographs. Every order includes tracking and is wrapped and robustly packaged with care in Tucson, AZ. ~Mesquite Booksellers.…

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Paperback. Condizione: new. Paperback. This book constitutes the refereed Proceedings of the 24th International Workshop on Multi-Agent-Based Simulation XXIV, MABS 2023, held in London, UK, during May 29June 2, 2023.The 11 regular papers presented were carefully reviewed and selected from 27 submissions. The papers are organized in subject areas as follows: MABS methodology and tools; MABS and social behavior; and MABS applications. This book constitutes the refereed Proceedings of the 24th International Workshop on Multi-Agent-Based Simulation XXIV, MABS 2023, held in London, UK, during May 29June 2, 2023.The 11 regular papers presented were carefully reviewed and selected from 27 submissions. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

hardcover. Condizione: Good. Bruise/tear to cover.

Condizione: New.

Condizione: New.

Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Hardcover. Condizione: new. New Copy. Customer Service Guaranteed.

Condizione: As New. Unread book in perfect condition.

Condizione: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condizione: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.

Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condizione: New. 2024th edition NO-PA16APR2015-KAP.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Hardcover. Condizione: New. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Hardcover. Condizione: new. Hardcover. A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review.This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition. A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: Brand New. 183 pages. 9.25x6.10x9.25 inches. In Stock.

Condizione: New. pp. 140.

Condizione: New.

Hardback. Condizione: New. second edition.