Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence

Book Description

Neuro-Fuzzy Modeling and Soft Computing places particular emphasis on the theoretical aspects of covered methodologies, as well as empirical observations and verifications of various applications in practice. Neuro-Fuzzy Modeling and Soft Computing is oriented toward methodologies that are likely to be of practical use. It includes exercises, some of which involve MATLAB programming tasks to provide readers with hands-on programming experiences for practical problem-solving. Each chapter also includes a reference list to the research literature so that readers may pursue topics in greater depth. This book is suitable as a self-study guide by researchers who want to learn basic and advanced neuro-fuzzy and soft computing within the framework of computational intelligence.

Customer Reviews:

Perfect !.......2001-12-26

A comprehensive guide concerned with understanding basics, modeling, analyzing Neuro-Fuzzy Networks. The examples and the illustraions are clear with a lot of Matlab codes. I recommend this book.

This is a very good introductory text on the subject........2000-03-29

The book provides a good overview to a wide disciplines of knowledge including fuzzy sets, neural nets, genetic algorithms and their composite use for developing high performance intelligent systems.The principles are explained with many examples and illustrations. The book is highly readable for its simplicity in presentation style. It is useful to anyone interested in this broad discipline.

Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)

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Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)

Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)
Lee Spector
Manufacturer: Kluwer Academic Publishers
ProductGroup: Book
Binding: Hardcover

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ASIN: 1402078943

Book Description

Automatic Quantum Computer Programming provides an introduction to quantum computing for non-physicists, as well as an introduction to genetic programming for non-computer-scientists. The book explores several ways in which genetic programming can support automatic quantum computer programming and presents detailed descriptions of specific techniques, along with several examples of their human-competitive performance on specific problems. Source code for the authorâ€™s QGAME quantum computer simulator is included as an appendix, and pointers to additional online resources furnish the reader with an array of tools for automatic quantum computer programming.

Customer Reviews:

Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming).......2007-01-12

It is a book that helps our students of doctorate.

Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer (Intelligent Robotics and Autonomous Agents)

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Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer (Intelligent Robotics and Autonomous Agents)
Peter Stone
Manufacturer: The MIT Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 0262194384

Book Description

This book looks at multiagent systems that consist of teams of autonomous agents acting in real-time, noisy, collaborative, and adversarial environments. The book makes four main contributions to the fields of machine learning and multiagent systems.

First, it describes an architecture within which a flexible team structure allows member agents to decompose a task into flexible roles and to switch roles while acting. Second, it presents layered learning, a general-purpose machine-learning method for complex domains in which learning a mapping directly from agents' sensors to their actuators is intractable with existing machine-learning methods. Third, the book introduces a new multiagent reinforcement learning algorithm--team-partitioned, opaque-transition reinforcement learning (TPOT-RL)--designed for domains in which agents cannot necessarily observe the state-changes caused by other agents' actions. The final contribution is a fully functioning multiagent system that incorporates learning in a real-time, noisy domain with teammates and adversaries--a computer-simulated robotic soccer team.

Peter Stone's work is the basis for the CMUnited Robotic Soccer Team, which has dominated recent RoboCup competitions. RoboCup not only helps roboticists to prove their theories in a realistic situation, but has drawn considerable public and professional attention to the field of intelligent robotics. The CMUnited team won the 1999 Stockholm simulator competition, outscoring its opponents by the rather impressive cumulative score of 110-0.

The Cognitive Approach to Conscious Machines

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The Cognitive Approach to Conscious Machines
Pentti O Haikonen
Manufacturer: Imprint Academic
ProductGroup: Book
Binding: Paperback

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ASIN: 0907845428

Book Description

The author argues that true conscious machines can be built, but rejects artificial intelligence and classical neural networks in favour of the emulation of the cognitive processes of the brain.
Novel views on consciousness and the mind-body problem are presented. This books is a must for anyone interested in consciousness research and the latest ideas in the forthcoming technology of mind.

Machine Learning: An Artificial Intelligence Approach (Volume I) (Machine Learning)

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Machine Learning: An Artificial Intelligence Approach (Volume I) (Machine Learning)
Ryszard S. Michalski , Jaime G. Carbonell , and Tom M. Mitchell
Manufacturer: Morgan Kaufmann
ProductGroup: Book
Binding: Hardcover

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Machine Learning

ASIN: 0934613095

Book Description

Multistrategy learning is one of the newest and most promising research directions in the development of machine learning systems. The objectives of research in this area are to study trade-offs between different learning strategies and to develop learning systems that employ multiple types of inference or computational paradigms in a learning process. Multistrategy systems offer significant advantages over monostrategy systems. They are more flexible in the type of input they can learn from and the type of knowledge they can acquire. As a consequence, multistrategy systems have the potential to be applicable to a wide range of practical problems. This volume is the first book in this fast growing field. It contains a selection of contributions by leading researchers specializing in this area.

Biometric Authentication: A Machine Learning Approach (Prentice Hall Information and System Sciences Series)

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Too much information, not enough detail

Biometric Authentication: A Machine Learning Approach (Prentice Hall Information and System Sciences Series)
S.Y. Kung , M.W. Mak , and S.H. Lin
Manufacturer: Prentice Hall PTR
ProductGroup: Book
Binding: Hardcover

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Guide to Biometrics (Springer Professional Computing)

ASIN: 0131478249

Customer Reviews:

Too much information, not enough detail.......2007-02-09

Any time you can pick up a used copy of a recently published technical book on an interesting topic at one-fourth of the retail price, you know there must be a problem. You would be right. This book tries to do three things at the same time and fails with at least two of its goals. It tries to talk about the business issues of biometrics, technical issues of specific biometric technologies (face recognition, speech recogniton, etc.), and finally machine learning techniques used to accomplish the biometric measurements. Only at this last goal do I think the book comes close to success, and even then only on a sketchy high level. The first seven chapters do an OK job of explaining machine learning techniques and give you some very instructive figures that are often lacking in academic textbooks, especially on neural networks. Also, these chapters do a pretty good job of explaining the equations involved. What's lacking, though, even in these early chapters, are some simple numerical examples or algorithmic steps that would give you some guidance on how to approach a task. When the book tries to make the leap to connecting the machine learning techniques to biometric authentication in a meaningful way such that a computer scientist could code up an algorithm, the book really falls on its face. There are some nice block diagrams of biometric systems, but no real details on algorithmic steps that would allow you to realize any of those blocks. Instead, there is quite a bit of verbage on the competition involved on building particular kinds of systems, and some rhetoric on possible pitfalls in specific biometric designs. However, with you standing there not knowing where to start with your design, this advice is really not very helpful.

I would say pass on this book and if you need to learn machine learning techniques, start with the older book by Mitchell entitled "Machine Learning". It talks about all of the machine learning techniques mentioned in this book, plus there are plenty of examples. Used copies are still relatively inexpensive, and its content is accessible and complete. As for biometric techniques, I've found the best books concentrate on one technique, such as fingerprint verification, and don't stray into other forms of authentication. The following is the table of contents:

Chapter 1. Overview
Chapter 2. Biometric Authentication Systems
Chapter 3. Expectation-Maximization Theory
Chapter 4. Support Vector Machines
Chapter 5. Multi-Layer Neural Networks
Chapter 6. Modular and Hierarchical Networks
Chapter 7. Decision-Based Neural Networks
Chapter 8. Biometric Authentication by Face Recognition
Chapter 9. Biometric Authentication by Voice Recognition
Chapter 10. Multicue Data Fusion
Appendix A: Convergence Properties of EM
Appendix B: Average Det Curves
Appendix C: Matlab Projects

Connectionist Approaches to Language Learning (The International Series in Engineering and Computer Science)

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Connectionist Approaches to Language Learning (The International Series in Engineering and Computer Science)

Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 0792392167

Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing (Lecture Notes in Computer Science)

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Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing (Lecture Notes in Computer Science)

Manufacturer: Springer
ProductGroup: Book
Binding: Paperback

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ASIN: 3540609253

Book Description

This book is based on the workshop on New Approaches to Learning for Natural Language Processing, held in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI'95, in Montreal, Canada in August 1995.
Most of the 32 papers included in the book are revised selected workshop presentations; some papers were individually solicited from members of the workshop program committee to give the book an overall completeness. Also included, and written with the novice reader in mind, is a comprehensive introductory survey by the volume editors. The volume presents the state of the art in the most promising current approaches to learning for NLP and is thus compulsory reading for researchers in the field or for anyone applying the new techniques to challenging real-world NLP problems.

Explanation-Based Neural Network Learning: A Lifelong Learning Approach (The International Series in Engineering and Computer Science)

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Explanation-Based Neural Network Learning: A Lifelong Learning Approach (The International Series in Engineering and Computer Science)
Sebastian Thrun
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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Book Description

Lifelong learning addresses situations in which a learner faces a series of different learning tasks providing the opportunity for synergy among them. Explanation-based neural network learning (EBNN) is a machine learning algorithm that transfers knowledge across multiple learning tasks. When faced with a new learning task, EBNN exploits domain knowledge accumulated in previous learning tasks to guide generalization in the new one. As a result, EBNN generalizes more accurately from less data than comparable methods. Explanation-Based Neural Network Learning: A Lifelong Learning Approach describes the basic EBNN paradigm and investigates it in the context of supervised learning, reinforcement learning, robotics, and chess. `The paradigm of lifelong learning - using earlier learned knowledge to improve subsequent learning - is a promising direction for a new generation of machine learning algorithms. Given the need for more accurate learning methods, it is difficult to imagine a future for machine learning that does not include this paradigm.' From the Foreword by Tom M. Mitchell.

Generating Abstraction Hierarchies: An Automated Approach to Reducing Search in Planning (The International Series in Engineering and Computer Science)

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Generating Abstraction Hierarchies: An Automated Approach to Reducing Search in Planning (The International Series in Engineering and Computer Science)
Craig A. Knoblock
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 0792393104

Book Description

Generating Abstraction Hierarchies presents a completely automated approach to generating abstractions for problem solving. The abstractions are generated using a tractable, domain-independent algorithm whose only inputs are the definition of a problem space and the problem to be solved and whose output is an abstraction hierarchy that is tailored to the particular problem. The algorithm generates abstraction hierarchies that satisfy the `ordered monotonicity' property, which guarantees that the structure of an abstract solution is not changed in the process of refining it. An abstraction hierarchy with this property allows a problem to be decomposed such that the solution in an abstract space can be held invariant while the remaining parts of a problem are solved. The algorithm for generating abstractions is implemented in a system called ALPINE, which generates abstractions for a hierarchical version of the PRODIGY problem solver. Generating Abstraction Hierarchies formally defines this hierarchical problem solving method, shows that under certain assumptions this method can reduce the size of a search space from exponential to linear in the solution size, and describes the implementation of this method in PRODIGY. The abstractions generated by ALPINE are tested in multiple domains on large problem sets and are shown to produce shorter solutions with significantly less search than problem solving without using abstraction. Generating Abstraction Hierarchies will be of interest to researchers in machine learning, planning and problem reformation.

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