A very useful book and quite a nice read. I bought the book after reading a few chapters. Even now, an year after my grad school, I still read this. A good reference.

Nice print, no mistakes, MATLAB code. You get everything on Kernel Methods, from theory to implementation. A perfect book and helped me a lot in my research.

Sloppy language, sloppy definitions, sloppy proofs.
Constant repetitions do not add any clarity either.

Sloppy language, sloppy definitions, sloppy proofs.
Constant repetitions do not add any clarity either.

Well, at first I was petrified to find a book that sounded like it deeply explores the subject of kernel methods. But all in all, it did not quite achieve what I hoped for. As a practical approach, when it comes to implementation, it serves nicely as a reference. The deeper mathematical roots of kernels (especially when it comes to measure theory and functional analysis) are not dealt with at all or just scratched at the very surface. The notation is sometimes awkward, mentioning for example the representation of an object in a given vector space with respect to the basis. And: Too much copied and pasted from the former book about SVMs. Basically, reading papers of Carmeli, Aronszajn and others will give you a much deeper insight into the subject.

This book will, without doubt, become THE reference work in kernel methods for pattern recognition, and a must read for pattern recognition researchers and practitioners in general.

It's built up in a nicely modular, accessible and didactive way, helping the reader understand thoroughly what kernel methods are all about and importantly, how to use them. This makes the book very useful as a cook book for practitioners, as well as a text book for students.

The book covers all the relevant topics in the state of the art of kernel methods, a field of research in which the authors have been a driving force since the beginning. Even so, they managed to resist the temptation from squeezing in the(ir) latest (potentially still unstable) results, which greatly enhances the timelessness of the book.

The book is divided into 3 parts. The theory is all in part I,
the rest of the book is a cook-book with plenty of matlab code.
The website contains most of the same code + data online. Readable, complete.

This work presents a coherent overview of an important field in machine learning. The unifying framework of kernel methods has proven state of the art results and the community has been waiting for a book like this to make both theory and practice of kernel methods accesssible for readers of all different backgrounds (researchers, students, practioners from both academia and industry, ...).

It is theoretically well-founded, the resulting algorithms are well-explained and made accessible for practioners by providing pseudo-code and online, ready-to-use matlab code.

This book nicely complements the previous, yellow book, written by the same authors. Indeed, after "getting into the field" by reading the accessible introduction to support vector machines (SVMs), it was clear to me that SVMs was only an example of a signifcantly larger framework, i.e., kernel methods. The blue book is the reference book about that larger framework I have been waiting for since then. I particularly like the way the book is set up, making clear the modular, flexible approach in kernel methods.

This book will, without doubt, become THE reference work in kernel methods for pattern recognition, and a must read for pattern recognition researchers and practitioners in general.

It's built up in a nicely modular, accessible and didactive way, helping the reader understand thoroughly what kernel methods are all about and importantly, how to use them. This makes the book very useful as a cook book for practitioners, as well as a text book for students.

The book covers all the relevant topics in the state of the art of kernel methods, a field of research in which the authors have been a driving force since the beginning. Even so, they managed to resist the temptation from squeezing in the(ir) latest (potentially still unstable) results, which greatly enhances the timelessness of the book.

The book is divided into 3 parts. The theory is all in part I,
the rest of the book is a cook-book with plenty of matlab code.
The website contains most of the same code + data online. Readable, complete.

I have never seen such an incredible useless peace of work. It is hard to imagine that the author fills whole of a book with basic linear algebra and a few trivialities and calls it "Kernel methods for pattern analysis". The book is a neverending want-to-be, but achieves nowhere in the discussion a level of maturity that even in some sense represents the state-of-the-art of research. It seems as if the author (J.S.-T.) has no deep knowledge of the matter and had nothing better to do than write this book to justify his existence. A lot of cut and paste from the other book of the authors. Putting it under a new cover doesn't make things better.
I give this book one star because of the beautiful fonts and the small amount of mistakes. Sadly, the book cannot be used as a source of toilet paper, because the paper is too glossy.

I have never seen such an incredible useless peace of work. It is hard to imagine that the author fills whole of a book with basic linear algebra and a few trivialities and calls it "Kernel methods for pattern analysis". The book is a neverending want-to-be, but achieves nowhere in the discussion a level of maturity that even in some sense represents the state-of-the-art of research. It seems as if the author (J.S.-T.) has no deep knowledge of the matter and had nothing better to do than write this book to justify his existence. A lot of cut and paste from the other book of the authors. Putting it under a new cover doesn't make things better.
I give this book one star because of the beautiful fonts and the small amount of mistakes. Sadly, the book cannot be used as a source of toilet paper, because the paper is too glossy.