Customer Reviews

Learning from Data: Concepts, Theory, and Methods (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)

5 star:		(3)
4 star:		(1)
3 star:		(0)
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The material contained in the textbook presents and discusses recent developments, but also important statistical (learning theory) concepts such as model selection, regularisation etc, in a unifying manner.
Although the authors are somewhat biased towards kernel methods, support vector machines in particular, they discuss the applicability and performance of other methods (neural networks, fuzzy systems, etc.). This is to be commended, as there are not many books that discuss all such methods in a common framework.
This book is highly recommended to readers wishing to gain a good understanding of the most significant statistical and other methods being applied in industry, and continuously experiencing significant academic research. A set of very good references (some mandatory and well known in the research community) presented at the end of each chapter directs the reader to some very useful material and scientific publications. This is a book that will particularly appeal to the research/academic community.

This book is excellent and easy to study. Graduate students will find the book statistical learning theory and support vector machines(SVMs),especially learning system based on recent advances in machine learning and multiobjective optimization. This book describes the Vapnik and Chervonenkis(VC) theory's generalization abilities. For statisticians, Applied mathematician, mechanical engineers and most graduate student are interested in reading this book. This is a very good excellent reference!!

This book introducing the general idea of learning from data, aka, machine leanring, data mining, etc, using a plain language. The algorithms and techniques described are very useful in pratice, although it may seems ad-hoc in the beginning. The whole field of statistical learning theory is very complicated (see the proceedings of COLT/ALT/etc). This book describes it in a straightforward and application-oriented way. Recommend to read. It is kind of pricey, though.

Beside the books of vapnik and scholkopf (and thorsten joachims book on svm text mining) i think this is a valuable book if one wants to learn more about machine learning and svm. The book presents a good overview and therefore i consider the book as a good starting point if one wants to study machine learning in depth.