Machine Learning Books - Page 12

Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)

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Editorial Review:

Adaptive Agents and Multi-Agent Systems is an emerging and exciting interdisciplinary area of research and development involving artificial intelligence, computer science, software engineering, and developmental biology, as well as cognitive and social science.

This book surveys the state of the art in this emerging field by drawing together thoroughly selected reviewed papers from two related workshops; as well as papers by leading researchers specifically solicited for this book. The articles are organized into topical sections on

- learning, cooperation, and communication

- emergence and evolution in multi-agent systems

- theoretical foundations of adaptive agents

Machine Learning Applications In Software Engineering (Series on Software Engineering and Knowledge Engineering)

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Editorial Review:

Machine learning deals with the issue of how to build computer programs that improve their performance at some tasks through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This book deals with the subject of machine learning applications in software engineering. It provides an overview of machine learning, summarizes the state-of-the-practice in this niche area, gives a classification of the existing work, and offers some application guidelines. Also included in the book is a collection of previously published papers in this research area.

The Language of Time: A Reader

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Customer Reviews:
Total reviews: 1 Average rating: 5.0 of 5

*The* source for learning about time in NL. 5 out of 5 stars.
1 of 1 people found this review helpful.

This is a collection of papers, starting from the classic Vendler on, including many useful for computational linguistics or NLP.

Editorial Review:

This reader collects and introduces important work in linguistics, computer science, artificial intelligence, and computational linguistics on the use of linguistic devices in natural languages to situate events in time: whether they are past, present, or future; whether they are real or hypothetical; when an event might have occurred, and how long it could have lasted. Clear, self-contained editorial introductions to each area provide the necessary technical background for the non-specialist, explaining the underlying connections across disciplines.

Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems)

Ian H. Witten, Eibe Frank

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Total reviews: 17 Average rating: 4.0 of 5

Editorial Review:

This book offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining-including both tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If you're involved at any level in the work of extracting usable knowledge from large collections of data, this clearly written and effectively illustrated book will prove an invaluable resource.

Complementing the authors' instruction is a fully functional platform-independent Java software system for machine learning, available for download. Apply it to the sample data sets provided to refine your data mining skills, apply it to your own data to discern meaningful patterns and generate valuable insights, adapt it for your specialized data mining applications, or use it to develop your own machine learning schemes.

* Helps you select appropriate approaches to particular problems and to compare and evaluate the results of different techniques.
* Covers performance improvement techniques, including input preprocessing and combining output from different methods.
* Comes with downloadable machine learning software: use it to master the techniques covered inside, apply it to your own projects, and/or customize it to meet special needs.

Introducing Speech and Language Processing (Cambridge Introductions to Language and Linguistics)

John Coleman

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Total reviews: 3 Average rating: 4.5 of 5

Unique and impressive contribution, but not bug-free 4 out of 5 stars.
1 of 1 people found this review helpful.

I've been using this as the textbook in my speech technology course at SJSU, with generally good results.

The book assumes that the reader is aquainted with basic acoustic and linguistic concepts such as glottal excitation, frequency spectra, fundamental frequency, and IPA transcription. The book is therefore not suitable on its own for a class of true beginners. For such an audience, instructors will want to supplement Coleman's book with a gentler introductory book such as Ladefoged (1996) or Johnson (2003).

By far the best feature of this book is its focus on concrete implementation, in source code, of the concepts discussed. If a picture is worth a thousand words, then a working C program is worth a thousand more. Coleman deserves our thanks for including actual speech processing code with his textbook.

The focus on explicit source code is one of a number of features that differentiate Coleman's textbook from the Jurafsky & Martin textbook that I use in other courses. Coleman's book is slimmer and less ambitious in its coverage of topics compared with Jurafsky & Martin's massive tome. Coleman's textbook also contains far fewer typos and other errors.

On the other hand, some parts of Coleman's book are frustratingly brief, incomplete, or opaque. An example is Section 4.2 on spectral analysis. The mechanics of the Hanning window are introduced, but without motivation---we never learn what the window is for, or why we need it. The results of the Fourier transform are displayed but no hint is offered as to how it works. Overall this section compares quite unfavorably to the masterful presentation of Fourier analysis in Chapter 10 of Ladefoged (1996).

Finally, it is important to note that there is a simple bug that infects most of the C programs supplied with the book. The variable "length", used to store the size of an input file, is declared as type "(int *)". This should be changed to type "int", so that memory is allocated to store the input size. Consequently, subsequent references to "*length" should be changed to "length", and "length" to "&length". Once this bug is fixed, the code compiles fine on any platform, not just on the compiler supplied with the book.

Editorial Review:

Assuming knowledge of only the very basics of linguistics, this major new textbook provides a concise and accessible introduction to speech and language processing. Students are introduced to topics such as digital signal processing, speech analysis and synthesis, finite-state machines, automatic speech recognition, parsing and probabilistic grammars, and are shown from a very elementary level how to work with two programming languages, C and Prolog.

Computation and Intelligence: Collected Readings

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Editorial Review:

Computation and Intelligence brings together 29 readings in Artificial Intelligence that are particularly relevant to today's student/practitioner. With its helpful critique of the selections, extensive bibliography, and clear presentation of the material, Computation and Intelligence will be a useful adjunct to any course in AI as well as a handy reference for professionals in the field.

The book is divided into five parts, each reflecting the stages of development of AI. The first part, Foundations,, contains readings that present or discuss foundational ideas linking computation and intelligence, typified by A. M. Turing's "Computing Machinery and Intelligence." The second part, Knowledge Representation, presents a sampling of numerous representational schemes by Newell, Minsky, Collins & Quillian, Winograd, Schank, Hayes, Holland, McClelland, Rumelhart, Hinton, and Brooks.

The third part, Weak Method Problem Solving, fouses on the research and design of syntax-based problem solvers, including the most famous of these, the Logic Theorist and GPS. The fourth part, Reasoning in Complex and Dynamic Environments, presents a broad spectrum of the AI community's research in knowledge-intensive problem solving, from McCarthy's early design of systems with "common sense" to model-based reasoning.

The two concluding selections, by Marvin Minsky and by Herbert Simon, respectively, present the recent thoughts of two of AI's pioneers who revisit the concepts and controversies that have developed during the evolution of the tools and techniques that make up the current practice of Artificial Intelligence.

Enter the Complexity Lab/Book and Disk

William H. Roetzheim

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Understanding Formal Methods (FACIT)

Jean-Francois Monin

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

concise and complete 5 out of 5 stars.
6 of 7 people found this review helpful.

This is a very well thought out book. The author reviews all the latest formal techniques and gives to the reader a complete outlook on this advanced subject in a clean and simple way. If you work in the field of critical software, you probably know some of them already, but if you're a developer dreaming of writing better code or making the machine write it for you, this book could be of some interest for you. A very good introdution to the most theoretic part of computer science.

Editorial Review:

This volume provides a comprehensive introduction to the field of formal methods for students and practitioners. It strikes a careful balance between rigorous exposition of the underlying mathematics and concrete examples of implementations using real-life tools, thus making it easy to grasp the underlying concepts and theories. It does not aim to provide guidelines for using a particular method, or comparisons of different approaches, but rather a conceptual framework that the reader can use to master any given method. It therefore makes an invaluable practical companion to introductory texts on logic and to books dedicated to a particular formal method. Understanding Formal Methods will be of interest to advanced students and engineers who need to learn the basics of this topic, and also professionals who need to broaden their knowledge or bring themselves up-to-date with the latest techniques.

Systems That Learn - 2nd Edition: An Introduction to Learning Theory (Learning, Development, and Conceptual Change)

Sanjay Jain, Daniel N. Osherson, James S. Royer, Arun Sharma

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Insightful introduction to computational learning theory 4 out of 5 stars.
2 of 3 people found this review helpful.

Concentrating on the mathematical and formal underpinnings of learning theory, this book gives a very interesting and general overview of the subject. Basing their discussion on the learning of "texts" and the learning of "functions", the authors address the main issues in the formal modeling of empirical inquiry. The models or "paradigms" they construct are based on five concepts, which they consider of central importance in empirical inquiry. These concepts are: (1) A reality that is theoretically possible; (2) hypotheses that are intelligible; (3) the data that is available about a given reality; (4) a model of a scientist; (5) the successful behavior of the scientist who is investigating a possible reality. The scientist is thought of as playing a game with Nature, with the class of possible realities being known to each of them initially. Nature selects a member from this class, initially unknown to the scientist. After providing a series of clues (data) to the scientist, the scientist forms hypotheses based on these clues. The scientist wins the game if the hypotheses become stable and accurate. The game is easier to win the more constrained Nature's choice of actual world is.

After a brief philosophical discussion of the paradigms and a review of the theory of computation, the authors begin their study by concentrating on identification of languages and identification of functions. Both of these are considered to be `theoretically possible realities', and in the case of languages, it is "texts" that are to be identified by scientists. Those texts that can are called `identifiable' and the authors prove a theorem that characterizes how scientists identify languages in terms of finite strings of text. Success in function identification is cast as a generalization of that of text. In this case the class of possible realities are the collection of total recursive functions, and hypotheses are programs that compute these functions. The authors show however that a scientist who identifies the entire class of recursive functions cannot be computable. Very interesting in this discussion is the treatment of `parametrized scientists', i.e. those scientists who can incorporate background knowledge from other scientists.

These considerations involve the view of a scientist as being a certain fixed entity. The authors also consider cases where alternative notions of scientist occur, but the other paradigms are held fixed. The abilities of computable scientists who deploy different inductive `strategies' are studied, with the goal of finding out if a member of a particular strategy can effectively identify languages or functions. Recognizing that the conjectures proposed successively by a particular scientist may not be related to one another, the authors then discuss strategies that result from imposing relations between the conjectures. One of these, called `conservative', insists that a conjecture that generates all the data observed to date should never be abandoned. Also discussed are `generalization strategies' that require scientists to improve upon their successive conjectures. One example of these strategies is called `strong-monotonic', which forbids the revision of a hypothesis if it made a mistake in identification. Another example is called `weak monotonic', which allows the rejection of parts of a hypothesis if it encounters data that cannot be accounted for by this hypothesis. Still another is `monotonic', which allows the correction of mistaken hypotheses, but does not allow hypotheses that will contradict correct classifications. The authors show that monotonicity does not imply weak-monotonicity, and vice versa. Also discussed are `specialization' strategies, which are "dual" to the three generalization strategies, and which involve the pruning of hypotheses in order to obtain convergence.

The authors also address the case where the conception of a scientist is held fixed, but the criteria for scientific success are varied. This study, in the opinion of this reviewer, more accurately reflects the real behavior of scientists, who typically use very liberal notions of accuracy. For example, anomalies in data could be tolerated, pending alteration of the hypotheses in the future. These anomalies in fact serve to drive further research, with the goal of finding hypotheses or theories that resolve them. It is typically the case, if not always, that the hypotheses are considered approximate explanations, and so one would expect that the authors' discussion would revolve around the consideration of inference of approximations. The authors though do give an interesting twist to this discussion, namely, they attempt to find criteria for success that actually permit an infinite number of anomalies in the final explanations. This serves to better characterize explanations, they argue. A series of identification criteria are outlined each of which involves measure-theoretic notions of `asymptotic agreement.' A scientist presented with a function must arrive at an explanation that agrees asymptotically with the function up to a prespecified amount.

Also more realistic, due to its emphasis on what happens in actual scientific investigation, is the authors' discussion on alternative conceptions of available data. Noting that data can be missing or have errors, and is presented in a definite order to a scientist, the authors study how to deal with error in the finding of intelligible hypotheses. Their results delineate the extent to which inaccurate data can impede the learning process, with three kinds of "inaccurate" data considered: "incomplete", "noisy", and "imperfect."

Other topics discussed include the modeling of empirical inquiry when many scientists are collaborating with each other, and that of probabilistic learning. For team identification of functions, several interesting results are proven, but the authors admit that their results do not apply to the (more realistic) scenario where the hypotheses of each individual scientist influence each other. Also discussed are "oracle" scientists, who use information of a noncomputable nature, or "information oracles", in order to perform identifications. When judged by how much information can be given to the scientist, oracles can be "omniscient" or "trivial", and it is thus of interest to determine how much oracles can supply scientists in their identification of functions. The authors discuss various results on this topic, showing how much is to be gained by allowing oracle scientists to make additional queries.

Editorial Review:

Formal learning theory is one of several mathematical approaches to the study of intelligent adaptation to the environment. The analysis developed in this book is based on a number theoretical approach to learning and uses the tools of recursive-function theory to understand how learners come to an accurate view of reality. This revised and expanded edition of a successful text provides a comprehensive, self-contained introduction to the concepts and techniques of the theory. Exercises throughout the text provide experience in the use of computational arguments to prove facts about learning.

Kernel Methods in Computational Biology (Computational Molecular Biology)

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Editorial Review:

Modern machine learning techniques are proving to be extremely valuable for the analysis of data in computational biology problems. One branch of machine learning, kernel methods, lends itself particularly well to the difficult aspects of biological data, which include high dimensionality (as in microarray measurements), representation as discrete and structured data (as in DNA or amino acid sequences), and the need to combine heterogeneous sources of information. This book provides a detailed overview of current research in kernel methods and their applications to computational biology.

Following three introductory chapters—an introduction to molecular and computational biology, a short review of kernel methods that focuses on intuitive concepts rather than technical details, and a detailed survey of recent applications of kernel methods in computational biology—the book is divided into three sections that reflect three general trends in current research. The first part presents different ideas for the design of kernel functions specifically adapted to various biological data; the second part covers different approaches to learning from heterogeneous data; and the third part offers examples of successful applications of support vector machine methods.