Handbook of Natural Language Processing Second Edition

Author: Nitin Indurkhya
Publisher: CRC Press
ISBN: 9781420085938
Format: PDF, Kindle
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The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis. New to the Second Edition Greater prominence of statistical approaches New applications section Broader multilingual scope to include Asian and European languages, along with English An actively maintained wiki (http://handbookofnlp.cse.unsw.edu.au) that provides online resources, supplementary information, and up-to-date developments Divided into three sections, the book first surveys classical techniques, including both symbolic and empirical approaches. The second section focuses on statistical approaches in natural language processing. In the final section of the book, each chapter describes a particular class of application, from Chinese machine translation to information visualization to ontology construction to biomedical text mining. Fully updated with the latest developments in the field, this comprehensive, modern handbook emphasizes how to implement practical language processing tools in computational systems.

Handbook of Natural Language Processing

Author: Robert Dale
Publisher: CRC Press
ISBN: 0824746341
Format: PDF, ePub
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This study explores the design and application of natural language text-based processing systems, based on generative linguistics, empirical copus analysis, and artificial neural networks. It emphasizes the practical tools to accommodate the selected system.

Machine Learning

Author: Stephen Marsland
Publisher: CRC Press
ISBN: 1498759785
Format: PDF, Mobi
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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.

Encyclopedia of Machine Learning

Author: Claude Sammut
Publisher: Springer Science & Business Media
ISBN: 0387307680
Format: PDF, ePub, Mobi
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This comprehensive encyclopedia, with over 250 entries in an A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of machine learning. Most entries in this preeminent work include useful literature references. Topics for the Encyclopedia of Machine Learning were selected by a distinguished international advisory board. These peer-reviewed, highly-structured entries include definitions, illustrations, applications, bibliographies and links to related literature, providing the reader with a portal to more detailed information on any given topic. The style of the entries in the Encyclopedia of Machine Learning is expository and tutorial, making the book a practical resource for machine learning experts, as well as professionals in other fields who need to access this vital information but may not have the time to work their way through an entire text on their topic of interest. The authoritative reference is published both in print and online. The print publication includes an index of subjects and authors. The online edition supplements this index with hyperlinks as well as internal hyperlinks to related entries in the text, CrossRef citations, and links to additional significant research.

A First Course in Machine Learning Second Edition

Author: Simon Rogers
Publisher: CRC Press
ISBN: 1498738567
Format: PDF
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"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

The Oxford Handbook of Computational Linguistics

Author: Ruslan Mitkov
Publisher: Oxford University Press
ISBN: 019927634X
Format: PDF, ePub, Docs
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A state-of-the-art reference to one of the most active and productive fields in linguistics: computational linguistics. Thirty-eight chapters, commissioned from experts all over the world, describe the major concepts, methods, and applications. Part I provides an overview of the field; Part II describes current tasks, techniques, and tools in natural language processing; and Part III surveys current applications.

Statistical Language Learning

Author: Eugene Charniak
Publisher: MIT Press
ISBN: 9780262531412
Format: PDF, Kindle
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This text introduces statistical language processing techniques—word tagging, parsing with probabilistic context free grammars, grammar induction, syntactic disambiguation, semantic word classes, word-sense disambiguation—along with the underlying mathematics and chapter exercises.

Taming Text

Author: Grant S. Ingersoll
Publisher: Manning Publications
ISBN: 9781933988382
Format: PDF, Docs
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Provides practical examples showing how to work with text in applications, covering such topics as proper name recognition, clustering, tagging, full-text search, and information extraction.

Cost Sensitive Machine Learning

Author: Balaji Krishnapuram
Publisher: CRC Press
ISBN: 143983928X
Format: PDF
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In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training data Cost of data annotation/labeling and cleaning Computational cost for model fitting, validation, and testing Cost of collecting features/attributes for test data Cost of user feedback collection Cost of incorrect prediction/classification Cost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost of learning into the modeling process. The first part of the book presents the theoretical underpinnings of cost-sensitive machine learning. It describes well-established machine learning approaches for reducing data acquisition costs during training as well as approaches for reducing costs when systems must make predictions for new samples. The second part covers real-world applications that effectively trade off different types of costs. These applications not only use traditional machine learning approaches, but they also incorporate cutting-edge research that advances beyond the constraining assumptions by analyzing the application needs from first principles. Spurring further research on several open problems, this volume highlights the often implicit assumptions in machine learning techniques that were not fully understood in the past. The book also illustrates the commercial importance of cost-sensitive machine learning through its coverage of the rapid application developments made by leading companies and academic research labs.

Machine Learning

Author: Stephen Marsland
Publisher: CRC Press
ISBN: 1466583339
Format: PDF, Kindle
Download Now
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.