Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)

von Bernhard Scholkopf ISBN: 0262194759 Kommentar abgeben

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.
Siehe auch:

• The Elements of Statistical Learning
• Pattern Classification
• An Introduction to Support Vector Machines and other kernel-based learning methods
• Computer Vision: A Modern Approach