Ben david shai (25 Ergebnisse)

Hardcover. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

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Paperback. Zustand: New. 1997 ed. 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.…

Zustand: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.…

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Hardcover. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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Zustand: As New. Unread book in perfect condition.

Zustand: As New. Unread book in perfect condition.

Hardback. Zustand: 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.…

Zustand: New.

Hardback. Zustand: 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.…

Zustand: As New. Unread book in perfect condition.

Taschenbuch. Zustand: 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.…

Taschenbuch. Zustand: 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. …

Hardcover. Zustand: Brand New. 397 pages. 10.00x7.00x1.00 inches. In Stock.

Gebunden. Zustand: 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.

Hardback. Zustand: 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.…

Paperback. Zustand: New. 1997 ed. 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.…

Hardback. Zustand: 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.…

Zustand: As New. Unread book in perfect condition.

Zustand: New. Covers the basic concepts of Artificial Intelligence and Machine Learning, methods and practices, and advanced topics and applications to clinical and precision medicinePresents readers with an understanding of how AI is revolutionizing med.

Zustand: New.

Taschenbuch. Zustand: Neu. Neuware - Artificial Intelligence for Medicine: An Applied Reference for Methods and Applications introduces readers to the methodology and AI/ML algorithms as well as cutting-edge applications to medicine, such as cancer, precision medicine, critical care, personalized medicine, telemedicine, drug discovery, molecular characterization, and patient mental health. Research in medicine and tailored clinical treatment are being quickly transformed by artificial intelligence (AI) and machine learning (ML). The content in this book is tailored to the reader's needs in terms of both type and fundamentals. It covers the current ethical issues and potential developments in this field.This book will be beneficial for academics, professionals in the IT industry, educators, students, and anyone else involved in the use and development of AI in the medical field.…

Zustand: new. Pages: 436 Language: Russian. Mashinnoe obuchenie - odin iz samykh bystro razvivajuschikhsja razdelov informatiki, s prilozhenijami v samykh raznykh oblastjakh. Tsel etoj knigi - poznakomit chitatelja s fundamentalnymi printsipami mashinnogo obuchenija i kharakternymi dlja nego algoritmicheskimi paradigmami. Kniga soderzhit obshirnyj svod osnovopolagajuschikh teoreticheskikh idej mashinnogo obuchenija i matematicheskie vykladki, blagodarja kotorym eti idei stanovjatsja prakticheskimi algoritmami. Vsled za izlozheniem bazovykh osnov distsipliny rassmatrivaetsja shirokij spektr tem, ne nashedshikh dostatochnogo otrazhenija v predshestvujuschikh uchebnikakh: vychislitelnaja slozhnost obuchenija, ponjatija vypuklosti i ustojchivosti, vazhnye algoritmy, vkljuchaja stokhasticheskij gradientnyj spusk, nejronnye seti i obuchenie strukturirovannomu vyvodu, a takzhe sovsem nedavnie teoreticheskie kon. Rekomenduem! 9785970606735.…