Statistical Mechanics of Neural Networks, Huang Haiping

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Цена: 76850.00 T
Наличие на складе: Заказано в издательстве.
Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

Автор: Ke-Lin Du; M. N. S. Swamy
Название: Neural Networks and Statistical Learning
ISBN: 1447170474 ISBN-13(EAN): 9781447170471
Издательство: Springer
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Цена: 95770.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Inclusive coverage of all the essential neural network applications in a statistical learning framework makes this a baseline text for students and researchers, with 25 chapters on all the major approaches that include a wealth of examples and exercises.

Автор: Ke-Lin Du; M. N. S. Swamy
Название: Neural Networks and Statistical Learning
ISBN: 1447174518 ISBN-13(EAN): 9781447174516
Издательство: Springer
Рейтинг:
Цена: 121110.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing.Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include:• multilayer perceptron;• the Hopfield network;• associative memory models;• clustering models and algorithms;• t he radial basis function network;• recurrent neural networks;• nonnegative matrix factorization;• independent component analysis;•probabilistic and Bayesian networks; and• fuzzy sets and logic.Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.

Автор: Helias, Moritz Dahmen, David
Название: Statistical field theory for neural networks
ISBN: 3030464431 ISBN-13(EAN): 9783030464431
Издательство: Springer
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Цена: 69870.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks.

Автор: Lotem Peled-Cohen, Roi Reichart, Rotem Dror, Segev Shlomov
Название: Statistical Significance Testing for Natural Language Processing
ISBN: 1681737973 ISBN-13(EAN): 9781681737973
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 66530.00 T
Наличие на складе: Нет в наличии.
Описание: Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has become rare to see an NLP paper, particularly one that proposes a new algorithm, that does not include extensive experimental analysis, and the number of involved tasks, datasets, domains, and languages is constantly growing. This emphasis on empirical results highlights the role of statistical significance testing in NLP research: If we, as a community, rely on empirical evaluation to validate our hypotheses and reveal the correct language processing mechanisms, we better be sure that our results are not coincidental.

The goal of this book is to discuss the main aspects of statistical significance testing in NLP. Our guiding assumption throughout the book is that the basic question NLP researchers and engineers deal with is whether or not one algorithm can be considered better than another one. This question drives the field forward as it allows the constant progress of developing better technology for language processing challenges. In practice, researchers and engineers would like to draw the right conclusion from a limited set of experiments, and this conclusion should hold for other experiments with datasets they do not have at their disposal or that they cannot perform due to limited time and resources. The book hence discusses the opportunities and challenges in using statistical significance testing in NLP, from the point of view of experimental comparison between two algorithms. We cover topics such as choosing an appropriate significance test for the major NLP tasks, dealing with the unique aspects of significance testing for non-convex deep neural networks, accounting for a large number of comparisons between two NLP algorithms in a statistically valid manner (multiple hypothesis testing), and, finally, the unique challenges yielded by the nature of the data and practices of the field.

Автор: Lotem Peled-Cohen, Roi Reichart, Rotem Dror, Segev Shlomov
Название: Statistical Significance Testing for Natural Language Processing
ISBN: 1681737957 ISBN-13(EAN): 9781681737959
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 48050.00 T
Наличие на складе: Нет в наличии.
Описание: Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has become rare to see an NLP paper, particularly one that proposes a new algorithm, that does not include extensive experimental analysis, and the number of involved tasks, datasets, domains, and languages is constantly growing. This emphasis on empirical results highlights the role of statistical significance testing in NLP research: If we, as a community, rely on empirical evaluation to validate our hypotheses and reveal the correct language processing mechanisms, we better be sure that our results are not coincidental.

The goal of this book is to discuss the main aspects of statistical significance testing in NLP. Our guiding assumption throughout the book is that the basic question NLP researchers and engineers deal with is whether or not one algorithm can be considered better than another one. This question drives the field forward as it allows the constant progress of developing better technology for language processing challenges. In practice, researchers and engineers would like to draw the right conclusion from a limited set of experiments, and this conclusion should hold for other experiments with datasets they do not have at their disposal or that they cannot perform due to limited time and resources. The book hence discusses the opportunities and challenges in using statistical significance testing in NLP, from the point of view of experimental comparison between two algorithms. We cover topics such as choosing an appropriate significance test for the major NLP tasks, dealing with the unique aspects of significance testing for non-convex deep neural networks, accounting for a large number of comparisons between two NLP algorithms in a statistically valid manner (multiple hypothesis testing), and, finally, the unique challenges yielded by the nature of the data and practices of the field.

Автор: Du Ke-Lin, Swamy M. N. S.
Название: Neural Networks and Statistical Learning
ISBN: 1447174542 ISBN-13(EAN): 9781447174547
Издательство: Springer
Рейтинг:
Цена: 93160.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework.

Автор: Pathria, R.k. (theoretical Physicist, University Of California, San Diego, Usa) Beale, Paul D. (professor Of Physics, University Of Colorado, Boulder,
Название: Statistical mechanics
ISBN: 0081026927 ISBN-13(EAN): 9780081026922
Издательство: Elsevier Science
Рейтинг:
Цена: 95390.00 T
Наличие на складе: Нет в наличии.
Описание: Discover new places with fully updated road atlases from Collins. Full colour map of Ireland at 9 miles to 1 inch (1:570,240), with clear, detailed road network and counties and new administrative areas shown in colour. This double-sided map covers the whole of Ireland, and is ideal for reference or route planning.

Автор: Yagawa Genki, Oishi Atsuya
Название: Computational Mechanics with Neural Networks
ISBN: 3030661105 ISBN-13(EAN): 9783030661106
Издательство: Springer
Цена: 179510.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other machine learning method in computational mechanics. Part II highlights the applications of neural networks to a variety of problems of computational mechanics.

Автор: Yagawa Genki, Oishi Atsuya
Название: Computational Mechanics with Neural Networks
ISBN: 303066113X ISBN-13(EAN): 9783030661137
Издательство: Springer
Рейтинг:
Цена: 158380.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other machine learning method in computational mechanics. Part II highlights the applications of neural networks to a variety of problems of computational mechanics.

Автор: Marco Pettini
Название: Geometry and Topology in Hamiltonian Dynamics and Statistical Mechanics
ISBN: 1441921648 ISBN-13(EAN): 9781441921642
Издательство: Springer
Рейтинг:
Цена: 102480.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book covers a new explanation of the origin of Hamiltonian chaos and its quantitative characterization. The subject of the book, which contains numerous illustrations throughout, is very original and nothing similar has been written hitherto.

Автор: Errico Presutti
Название: Scaling Limits in Statistical Mechanics and Microstructures in Continuum Mechanics
ISBN: 3642092365 ISBN-13(EAN): 9783642092367
Издательство: Springer
Рейтинг:
Цена: 83850.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Collective behavior in systems with many components, blow-ups with emergence of microstructures are proofs of the double, continuum and atomistic, nature of macroscopic systems, an issue which has always intrigued scientists and philosophers.