Learning from Data : Concepts, Theory, and Methods

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Summary: An up to date, unifying textbook on learning/modelling depen
Review: 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.

Rating:
Summary: Study in easy
Review: 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!!