Автор: Kevin Murphy Название: Machine Learning ISBN: 0262018020 ISBN-13(EAN): 9780262018029 Издательство: Random House (USA) Рейтинг: Цена: 11229 р. Наличие на складе: Есть у поставщика Поставка под заказ.
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.
The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Автор: Christopher M. Bishop Название: Pattern Recognition and Machine Learning ISBN: 0387310738 ISBN-13(EAN): 9780387310732 Издательство: Springer Рейтинг: Цена: 8881 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.A forthcoming companion volume will deal with practical aspects of pattern recognition and machine learning, and will include free software implementations of the key algorithms along with example data sets and demonstration programs.Christopher Bishop is Assistant Director at Microsoft Research Cambridge, and also holds a Chair in Computer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, and was recently elected Fellow of the Royal Academy of Engineering. The author's previous textbook "Neural Networks for Pattern Recognition" has been widely adopted.Coming soon:*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)*For instructors, worked solutions to remaining exercises from the Springer web site*Lecture slides to accompany each chapter*Data sets available for download
Автор: Munday Jeremy Название: Introducing Translation Studies ISBN: 1138912557 ISBN-13(EAN): 9781138912557 Издательство: Taylor&Francis Рейтинг: Цена: 1502 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Introducing Translation Studies remains the definitive guide to the theories and concepts that make up the field of translation studies. Providing an accessible and up-to-date overview, it has long been the essential textbook on courses worldwide.
This fourth edition has been fully revised and continues to provide a balanced and detailed guide to the theoretical landscape. Each theory is applied to a wide range of languages, including Bengali, Chinese, English, French, German, Italian, Punjabi, Portuguese and Spanish. A broad spectrum of texts is analysed, including the Bible, Buddhist sutras, Beowulf, the fiction of Garcia Marquez and Proust, European Union and UNESCO documents, a range of contemporary films, a travel brochure, a children's cookery book and the translations of Harry Potter.
Each chapter comprises an introduction outlining the translation theory or theories, illustrative texts with translations, case studies, a chapter summary and discussion points and exercises.
NEW FEATURES IN THIS FOURTH EDITION INCLUDE:
new material to keep up with developments in research and practice, including the sociology of translation, multilingual cities, translation in the digital age and specialized, audiovisual and machine translation
revised discussion points and updated figures and tables
new, in-chapter activities with links to online materials and articles to encourage independent research
an extensive updated companion website with video introductions and journal articles to accompany each chapter, online exercises, an interactive timeline, weblinks, and powerpoint slides for teacher support
This is a practical, user-friendly textbook ideal for students and researchers on courses in Translation and Translation Studies.
Описание: Delivering a thoroughly revised and updated version of the most authoritative reference work in the field, this edition draws on the expertise of over 90 contributors from all over the world, providing an unparalleled global perspective which makes this volume unique.
Автор: Munday Jeremy Название: Evaluation in Translation ISBN: 0415577705 ISBN-13(EAN): 9780415577700 Издательство: Taylor&Francis Рейтинг: Цена: 4850 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In this book, Jeremy Munday presents advances towards a general theory of evaluation in translator decision-making that will be of high importance to translator and interpreter training and to descriptive translation analysis. By ‘evaluation’ the author refers to how a translator’s subjective stance manifests itself linguistically in a text. In a world where translation and interpreting function as a prism through which opposing personal and political views enter a target culture, it is crucial to investigate how such views are processed and sometimes subjectively altered by the translator. To this end, the book focuses on the translation process (rather than the product) and strives to identify more precisely those points where the translator is most likely to express judgment or evaluation. The translations studied cover a range of languages (Arabic, Chinese, Dutch, French, German, Indonesian, Italian, Japanese, Russian, Spanish and American Sign Language) accompanied by English glosses to facilitate comprehension by readers. This is key reading for researchers and postgraduates studying translation theory within Translation and Interpreting Studies.
Описание: A practical guide to conference interpreting by an experienced interpreter working for the European Commission. It contains detailed discussion of the pitfalls and strategies of the work, as well as relevant contacts.
Автор: Masashi Sugiyama Название: Introduction to Statistical Machine Learning ISBN: 0128021217 ISBN-13(EAN): 9780128021217 Издательство: Elsevier Science Рейтинг: Цена: 10659 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. . Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks.
Автор: Ed. by L. Venuti Название: The Translation Studies Reader ISBN: 0415187478 ISBN-13(EAN): 9780415187473 Издательство: Taylor&Francis Цена: 2424 р. Наличие на складе: Поставка под заказ.
Описание: This text guides the reader through the varying approaches to translation studies in the latter half of the 20th century. Chronologically ordered and divided into clear sections, Lawrence Venuti has gathered key essays, articles and book extracts together in one volume, thus providing a clear history of translation studies. The text also covers contemporary translation research and analysis, and, as it approaches the end of the 20th century, offers glimpses of possible future trends. Venuti introduces each section with comments on the readings and influential theorists, sketches the main theoretical trends in the period and offers critical assessment. Tbook should be useful as a course textbook and a stimulus for further research.
Автор: Marsland Название: Machine Learning ISBN: 1466583282 ISBN-13(EAN): 9781466583283 Издательство: Taylor&Francis Рейтинг: Цена: 6582 р. Наличие на складе: Поставка под заказ.
Описание: A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. New to the Second Edition Two new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of content Revision of the support vector machine material, including a simple implementation for experiments New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron Additional discussions of the Kalman and particle filters Improved code, including better use of naming conventions in Python Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.
Описание: "A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC."—Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden "This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade."—Daniel Barbara, George Mason University, Fairfax, Virginia, USA "The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts."—Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark "I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength…Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."—David Clifton, University of Oxford, UK "The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book."?—Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK "This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning…The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective."—Guangzhi Qu, Oakland University, Rochester, Michigan, USA
Описание: Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn’t prove theorems, or otherwise dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core machine learning topics in-depth, including Hidden Markov Models, Principal Component Analysis, Support Vector Machines, and Clustering. It also includes coverage of Nearest Neighbors, Neural Networks, Boosting and AdaBoost, Random Forests, Linear Discriminant Analysis, Vector Quantization, Naive Bayes, Regression Analysis, Conditional Random Fields, and Data Analysis. Most of the examples in the book are drawn from the field of information security, with many of the machine learning applications specifically focused on malware. The applications presented are designed to demystify machine learning techniques by providing straightforward scenarios. Many of the exercises in this book require some programming, and basic computing concepts are assumed in a few of the application sections. However, anyone with a modest amount of programming experience should have no trouble with this aspect of the book. Instructor resources, including PowerPoint slides, lecture videos, and other relevant materialare provided on an accompanying website: http://www.cs.sjsu.edu/~stamp/ML/. For the reader’s benefit, the figures in the book are also available in electronic form, and in color. About the Author Mark Stamp has been a Professor of Computer Science at San Jose State University since 2002. Prior to that, he worked at the National Security Agency (NSA) for seven years, and a Silicon Valley startup company for two years. He received his Ph.D. from Texas Tech University in 1992. His love affair with machine learning began in the early 1990s, when he was working at the NSA, and continues today at SJSU, where he has supervised vast numbers of master’s student projects, most of which involve a combination of information security and machine learning.
Описание: This book constitutes the refereed proceedings of the 5th Conference of the Association for Machine Translation in the Americas, AMTA 2002, held in Tiburon, CA, USA, in October 2002.The 18 revised full technical papers, 3 user studies, and 9 system descriptions presented were carefully reviewed and selected for inclusion in the book. Among the issues addressed are hybrid translation environments, resource-limited MT, statistical word-level alignment, word formation rules, rule learning, web-based MT, translation divergences, example-based MT, data-driven MT, classification, contextual translation, the lexicon building process, commercial MT systems, speeck-to-speech translation, and language checking systems.
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