Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) 1st Edition by Bernhard Schlkopf (PDF)

Description

A comprehensive introduction to Support Vector Machines and related kernel methods.In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs―-kernels―for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

User’s Reviews

Editorial Reviews: Review This splendid book fills the need for a comprehensive treatment of kernel methods and support vector machines. It collects results, theorems, and discussions from disparate sources into one very accessible exposition. I am particularly impressed that the authors have included problem sets at the end of each chapter; such problems are not easy to construct, but add significantly to the value of the book for the student audience.―Chris J. C. Burges, Microsoft Research About the Author Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐This book wanted to be the comprehensive discussion about SVM and related topics, but reality kicked in, and many topics had to be left out. What is here, though, is a thorough coverage of the most important areas. The authors do note at the beginning of each chapter which sections are essential, and which can be omitted at first reading. If you want to know about any specific area of SVM, it is probably here, and covered in depth. The authors are acknowledged experts in the field, and it shows.I started out dabbling with SVM, and turned to this book when I really needed to know what was going on. I found everything that I needed, and much more. Having a Ph.D. in mathematics helped with that side of the text, but found myself a bit overwhelmed with the statistics, a field that has numerous technical terms. The appendix on stats was insufficient. And learning theory was completely new to me. But as it turned out, neither was essential to understanding the rest of the book. The book has numerous examples, and the real learning happens there. I had fun reading the book, but my idea of fun is something most people would away from, screaming.The book has a gigantic bibliography, and the authors constantly refer to it. If you want to explore further in any particular direction, you will know where to start looking.There are a few typos, as can be expected in a book this large, and one theorem that is false as stated. (Theorem 4.1 needs the hypothesis of convexity added.) But the theorem is never actually used; it is just an illustration. One thing that I would have liked is some Web site for the book where these things can be brought up.

⭐I especially like the one class SVM algorithm invented by the author so I studied about two-thirds of the book very diligently. The math behind is a bit hard but still digestible. Strongly recommend for those who enjoy math and machine learning.

⭐To start with, this book is rather technical with many theorems, assumptions, lengthy discussions. At the same time, it allows a beginner to start with some chapters and start exploring this field – from understanding point of view and even practical aspects. That was the case for me, started with suggested sections, i understood why this or that theorem is essential and what is the reasoning behind.I find this book rather deep in learning with kernels and i suggest it as a reading for starting phD (and master?) students to get in grasp with concepts. And then delve into more details.The book itself is nicely organized by topics and is not at all to be read chapter by chapter. Authors give some guidelines what to start according to your prior knowledge or experience.I take one star off to show that “may be” there was a way to write about the same complex and fast developing domain better and easier to grasp for a reader 🙂

⭐This book is dedicated almost entirely to support vector machines for pattern recognition. This is not really an introductory text to machine learning though. For that I would recommend Statistical Learning Theory by Vapnik or Neural Networks and Learning Machines by Haykin. However, this book is starting to get a little bit dated as the field continues to push forward.This book presents a very deep mathematics to backup the theory of SVMs. This includes reproducing kernel Hilbert spaces, functional analysis and probability. If you are not familiar with these concepts already, you probably are not ready for this book.The appendices are great references, assuming you already have the necessary background.

⭐It was written by two of the kernel machine pioneers. It’s a very good introductory monolog on kernel methods and an excellent reference book for anyone who wants to learn the fundamentals behind the kernel tricks and their applications.One may need an intermediate level of mathematics and linear algebra to understand the derivations and kernel designing ideas.

⭐It is the best book on kernel methods. It covers a wide range of subjects.The best thing is that after finishing one or two basic chapters, you can read the rest of the book in any order; most chapters are almost independent to each other. At the beginning of a chapter, the authors list the prerequistites, so a reader knows whether he will be able to understand the chapter.For now the book still reflects the state of art. But it is a fast changing field. I hope the authors will update the book in the future.

⭐Satisfied

⭐meet my expectation.

⭐Libro completo pero bastante denso y farragoso que no es precisamente para principiantes.Eine sehr gute Behandlung der kernel-basierten Lernverfahren. Die Autoren bemühen sich, dem Leser stets den roten Faden aufzuzeigen. Die Kapitel sind mehr oder weniger eigenständig gehalten, wobei manches Resultat wiederholt wird. Dadurch ist es aber möglich, dass man auch springen kann.Die Erklärungen versuchen die Intuition anzusprechen, aber die Sachverhalte werden auch mathematisch hergeleitet.

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