Free PDF Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems), by Vojislav Kecm
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Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems), by Vojislav Kecm
Free PDF Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems), by Vojislav Kecm
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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.
- Sales Rank: #1231097 in Books
- Published on: 2001-03-19
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x 1.25" w x 7.00" l, 2.71 pounds
- Binding: Hardcover
- 608 pages
Review
Kecman has many years of teaching and research experience, so naturally he does an excellent job of presenting the essence of learning and soft computing using neural networks, fuzzy logic, and statistics.
(Zoran Gajic, Department of Electrical and Computer Engineering, Rutgers University)This book provides an excellent in-depth description of modern learning and soft computing methodologies. Accompanying software implementation of learning algorithms makes this text especially valuable for practitioners and graduate students interested in applications of predictive learning.
(Vladimir Cherkassky, Department of Electrical and Computer Engineering, University of Minnesota, Twin Cities)This outstanding volume unifies the concepts of learning, neural networks, support vector machines, and fuzzy logic! It offers a clear presentation and numerous examples followed by end-of-chapter problems. These things along with the accompanying software make the book a favorite candidate for the leading academic text and an indispensable reference for soft computing professionals.
(Jacek M. Zurada, S.T. Fife Professor of Electrical and Computer Engineering, University of Louisville, and Editor-in-Chief, IEEE Transactions on Neural Networks) About the Author
Vojislav Kecman is Professor in the Computer Science Department at Virginia Commonwealth University, Richmond, VA.
Most helpful customer reviews
8 of 8 people found the following review helpful.
An extremely good book
By amazonbuyerseller
This is a very good book. Another reviewer has commented on Vojislav Kecman being an excellent teacher. I whole-heartedly second that opinion. Often times, while reading this book, you will pause with a doubt or question. What you will find surprising is that almost certainly the author has answered that question in the next paragraph. Many times, the author's answers will tally your own answers.
The first chapter of the book (entitled: Learning and Soft Computing: Rationale, Motivations, Needs, Basics) is 119 pages long. It is an essential reading. By the time you finish reading this chapter the things will start falling into place and you will be more motivated and ready to read the remaining chapters. Until you are highly aware of this topic, do not skip this chapter.
A book is made up of a lot of things other than the text that it covers. Does it contain many/any stupid jokes? Is it printed on the highest quality paper? Is the font size good? Is it printed too dense? Is the cover page inviting enough? Are the dimensions/weight of the book correct? On all these counts the book scores high.
Consistent with the subject matter that it covers, this is not an easy book. You will perhaps like to read it with paper and pencil. But if you are willing to spend time with this book, this book will do a lot of good to you. This is a very good book.
20 of 21 people found the following review helpful.
An excellent book on Machine Learning
By lucy
What strikes me each time I open this book is Mr Kecman's sense of pedagogy: it is a lesson in the matter. Not only his book delivers the - sometimes complex - techniques in a highly readable manner, but the concepts behind each of the main tools (SVM, NN & FL) he chose to highlight are always brilliantly put in context. One comes out of the reading with more than a set of equations but rather with a clearer picture of the field.
Mr Kecman is - without a doubt - a great teacher.
This effort to deliver a clear message is furthermore underlined through the numerous original figures: if you are like me and feel that a (good) picture speaks more than a thousand words, you will sure appreciate the way the illustrations complement the text and truly help the understanding.
I have read several other books on the subject but if I had to chose one for teaching purposes, this would be the one. I you want to build a better understanding of the field, get this book: it will pay on the long term.
23 of 27 people found the following review helpful.
Excellent, useful book!
By Dragoljub Pokrajac
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....
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