Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)
more than an introduction
|von Bernhard Scholkopf|
If you would like to know what pattern recognition is,
and how to solve this kind of problems with standard
state-of-the-art tools from statistical learning
theory or with a bayesian approach,
this is the book you can start with, even if you are not
familiar with this technics.
This splendid book is easy to read,
starts with a simple introduction and
continues with basic concepts and tools,
like kernels,risk minimization,regularization,
elements of statistical learning theory and optimization.
Thereafter they discribe the support vector machine
in a detail manner one of the state-of-the-art tools
for regression and classification.
And they finish with kernel methods, like the kernel pca,
kernel fisher discriminant, bayesian kernel methods and pre-images.
So it collects results, theorems,
algorithms and discussions from different sources into one
very accessible exposition.
It is also a good reference book.
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