Machine Learning Books - Page 6

Case-Based Reasoning: Experiences, Lessons, and Future Directions

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Total reviews: 1 Average rating: 4.0 of 5

Editorial Review:

Case-based reasoning (CBR) is a flourishing paradigm for reasoning and learning in artificial intelligence, with major research efforts and burgeoning applications extending the frontiers of the field.

This book provides an introduction for students as well as an up-to-date overview for experienced researchers and practitioners. It examines the field in a "case-based" way, through concrete examples of how key issues—including indexing and retrieval, case adaptation, evaluation, and application of CBR methods—are being addressed in the context of a range of tasks and domains. Complementing these case studies are commentaries by leading researchers on the lessons learned from experiences with CBR and visions for the roles in which case-based reasoning can have the greatest impact.

A tutorial introduction by Janet Kolodner, one of the originators of CBR, and David Leake makes the book accessible to students and developers starting to apply case-based reasoning. The volume can also serve as a suitable companion for a CBR or introductory AI textbook.

Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms (The Springer International Series in Engineering and Computer Science)

Thorsten Joachims

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Customer Reviews:
Total reviews: 2 Average rating: 5.0 of 5

Wonderful book on the subject 5 out of 5 stars.
1 of 8 people found this review helpful.

This is a Tesis Work, it contains a review and conmparation of several learners. It focuses mainly on SVM.

The Gold standard 5 out of 5 stars.
1 of 1 people found this review helpful.

This is a must read for anyone beginning to investigate the analysis of meaning in text using computational methods. I found the initial sections were useful in bringing together my thought on many different aspects of the topic.

Editorial Review:

Presents a new approach to generating text classifiers from examples. Combines high performance and efficiency with theoretical understanding and improved relationship in particular, and gives a complete and detailed explanation of the SVM approach to learning text classifiers.

Natural Language Processing and Text Mining

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Editorial Review:

With the increasing importance of the Web and other text-heavy application areas, the demands for and interest in both text mining and natural language processing (NLP) have been rising. Researchers in text mining have hoped that NLP—the attempt to extract a fuller meaning representation from free text—can provide useful improvements to text mining applications of all kinds.

Bringing together a variety of perspectives from internationally renowned researchers, Natural Language Processing and Text Mining not only discusses applications of certain NLP techniques to certain Text Mining tasks, but also the converse, i.e., use of Text Mining to facilitate NLP. It explores a variety of real-world applications of NLP and text-mining algorithms in comprehensive detail, placing emphasis on the description of end-to-end solutions to real problems, and detailing the associated difficulties that must be resolved before the algorithm can be applied and its full benefits realized. In addition, it explores a number of cutting-edge techniques and approaches, as well as novel ways of integrating various technologies. Nevertheless, even readers with only a basic knowledge of data mining or text mining will benefit from the many illustrative examples and solutions.

Topics and features:

• Describes novel and high-impact text mining and/or natural language applications

• Points out typical traps in trying to apply NLP to text mining

• Illustrates preparation and preprocessing of text data – offering practical issues and examples

• Surveys related supporting techniques, problem types, and potential technique enhancements

• Examines the interaction of text mining and NLP

This state-of-the-art, practical volume will be an essential resource for professionals and researchers who wish to learn how to apply text mining and language processing techniques to real world problems. In addition, it can be used as a supplementary text for advanced students studying text mining and NLP.

Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems)

Vojislav Kecman

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Total reviews: 3 Average rating: 5.0 of 5

Excellent, useful book! 5 out of 5 stars.
20 of 24 people found this review helpful.

This book is a nice and, I would say, a successful attempt to provide a unified survey of important theoretical and practical machine learning tools: neural networks (NN), support vector machines (SVM) and fuzzy systems (FS).

Book consists of nine chapters, covering SVMs, one- and multi-layer perceptrons and radial-basis function networks, as variants of neural networks, and basics of fuzzy theory. This is followed by interesting case-studies (in financial, control and computer graphic applications) and concluded by basics of optimization theory and an overview of necessary mathematical tools. All the MATLAB programs needed for the simulated experiments are available on the book web site.

Authored by Vojislav Kecman, a prominent researcher in the field of soft computing and previous MIT visiting professor, this book is an excellent material for advanced undergraduate and introductory graduate courses in machine learning applications and soft computing....

Editorial Review:

This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.

Lattice-Gas Cellular Automata and Lattice Boltzmann Models: An Introduction (Lecture Notes in Mathematics)

Dieter A. Wolf-Gladrow

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Total reviews: 2 Average rating: 4.5 of 5

Computational Models of Physical Processes 5 out of 5 stars.
6 of 6 people found this review helpful.

This is an outstanding book for anyone concerned with computational models of real physical processes (digital mechanics or digital physics). It is accessible to anyone with a bachelor's degree in computer science, mathematics, engineering, or physics. It is also accessible to others with a few years of math courses. Lattice gasses and lattice boltzmann machines are the natural extensions of cellular automata such as the game of life.

Editorial Review:

Lattice-gas cellular automata (LGCA) and lattice Boltzmann models (LBM) are relatively new and promising methods for the numerical solution of nonlinear partial differential equations. The book provides an introduction for graduate students and researchers. Working knowledge of calculus is required and experience in PDEs and fluid dynamics is recommended. Some peculiarities of cellular automata are outlined in Chapter 2. The properties of various LGCA and special coding techniques are discussed in Chapter 3. Concepts from statistical mechanics (Chapter 4) provide the necessary theoretical background for LGCA and LBM. The properties of lattice Boltzmann models and a method for their construction are presented in Chapter 5.

Plans and Situated Actions: The Problem of Human-Machine Communication (Learning in Doing: Social, Cognitive and Computational Perspectives)

Lucy A. Suchman

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Total reviews: 5 Average rating: 4.0 of 5

Editorial Review:

This lively and original book offers a provocative critique of the dominant assumptions regarding human action and communication which underlie recent research in machine intelligence. Lucy Suchman argues that the planning model of interaction favoured by the majority of AI researchers does not take sufficient account of the situatedness of most human social behaviour. The problems that can arise as a result are pertinently, and often amusingly, illustrated by the careful analysis of a recorded interaction between novice users and an intelligent machine, whose design has failed to accommodate essential resources of successful human communication. Plans and Situated Actions presents a compelling case for the re-examination of current models underlying interface design. Lucy Suchman's proposals for a fresh characterisation of human-computer interaction which also incorporates recent insights from the social sciences provides a challenge that everyone interested in machine intelligence will seriously need to consider.

Principles of Data Mining (Adaptive Computation and Machine Learning)

David J. Hand, Heikki Mannila, Padhraic Smyth

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Total reviews: 17 Average rating: 3.5 of 5

Editorial Review:

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.

The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

Blondie24: Playing at the Edge of AI (The Morgan Kaufmann Series in Artificial Intelligence)

David B. Fogel

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Total reviews: 36 Average rating: 4.5 of 5

Editorial Review:

Blondie24 tells the story of a computer that taught itself to play checkers far better than its creators ever could by using a program that emulated the basic principles of Darwinian evolution--random variation and natural selection-- to discover on its own how to excel at the game.

Unlike Deep Blue, the celebrated chess machine that beat Garry Kasparov, the former world champion chess player, this evolutionary program didn't have access to strategies employed by human grand masters, or to databases of moves for the endgame moves, or to other human expertise about the game of chekers. With only the most rudimentary information programmed into its "brain," Blondie24 (the program's Internet username) created its own means of evaluating the complex, changing patterns of pieces that make up a checkers game by evolving artificial neural networks---mathematical models that loosely describe how a brain works.

It's fitting that Blondie24 should appear in 2001, the year when we remember Arthur C. Clarke's prediction that one day we would succeed in creating a thinking machine. In this compelling narrative, David Fogel, author and co-creator of Blondie24, describes in convincing detail how evolutionary computation may help to bring us closer to Clarke's vision of HAL. Along the way, he gives readers an inside look into the fascinating history of AI and poses provocative questions about its future.

* Brings one of the most exciting areas of AI research to life by following the story of Blondie24's development in the lab through her evolution into an expert-rated checkers player, based on her impressive success in Internet competition.
* Explains the foundations of evolutionary computation, simply and clearly.
* Presents complex material in an engaging style for readers with no background in computer science or artificial intelligence.
* Examines foundational issues surrounding the creation of a thinking machine.
* Debates whether the famous Turing Test really tests for intelligence.
* Challenges deeply entrenched myths about the successes and implication of some well-known AI experiments
* Shows Blondie's moves with checkerboard diagrams that readers can easily follow.

Introduction to Formal Languages and Automata

Peter Linz

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Total reviews: 29 Average rating: 2.5 of 5

Simply godawful 1 out of 5 stars.
4 of 7 people found this review helpful.

I had to purchase this for my school's Intro to CS Theory course.

Linz' utter ineptitude towards writing is what gives this book 1 star. Examples throughout chapters are sparse and relatively worthless. Sample problems at the end of the chapter, in contrast, are ridiculously difficult, and the solutions in the back don't offer any explanation whatsoever towards the answers.

This is the only book I have ever read that actually made me feel dumber for reading it. It's simply demeaning. Rather than explaining or justifying his logic, as he should to the target audience of this book, he simply uses "it's obvious that..." repeatedly for sample problems and solutions. A ridiculously complex problem's solution in the back of the book will be whittled down to two lines at best, half of which says something along the line of "It's blatantly obvious that the answer is ___, and you're stupid for not realizing it."

If you're actually assigned graded work from this book, may god have mercy on your soul.

Editorial Review:

This text covers all the material essential to an introductory theory of computation course for undergraduate students. The text has a solid mathematical base, and provides precise mathematical statements of theorems and definitions, giving an intuitive motivation for constructions and proofs. Proofs and arguments are clearly stated, without excessive mathematical detail, to help students understand the basic principles. The text is illustrated with integrated examples of new concepts as well as an abundance of exercises to aid in the development of problem solving skills.

Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning)

Ralf Herbrich

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Editorial Review:

Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comprehensively studied model—and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.