Optimization. Linear Classification Machines. Linear Regression Machines. Kernels and Support Vector Machines. Basic Statistical Learning Theory of C-Support Vector Classification. Model Construction. Implementation. Variants and Extensions of Support Vector Machines. Bibliography. Index.
Naiyang Deng, Yingjie Tian, Chunhua Zhang
This book provides a concise overview of support vector machines
(SVMs), starting from the basics and connecting to many of their
most significant extensions. Starting from an optimization
perspective provides a new way of presenting the material,
including many of the technical details that are hard to find in
other texts. And since it includes a discussion of many practical
issues important for the effective use of SVMs (e.g., feature
construction), the book is valuable as a reference for researchers
and practitioners alike.
—Thorsten Joachims, Associate Professor, Department of Computer
Science, Cornell UniversityThe books on support vector machines
(SVMs) in Chinese written by the same authors are very popular in
China. It is really great that the authors have translated the
books into English and made further extensions on them. One thing
which makes the book very unique from the other books is that the
authors try to shed light on SVM from the viewpoint of
optimization. I believe that the comprehensive and systematic
explanation on the basic concepts, fundamental principles,
algorithms, and theories of SVM will help readers have a really
in-depth understanding of the space. It is really a great book,
which many researchers, students, and engineers in computer science
and related fields will want to carefully read and routinely
consult.
—Dr. Hang Li, Chief Scientist of Noah’s Ark Lab, Huawei
Technologies Co., LtdThis book comprehensively covers many topics
of support vector machines (SVMs). In particular, it gives a nice
connection between optimization theory and support vector machines.
In my experience of developing the popular SVM software LIBSVM, I
found that many users lack a good understanding of the optimization
concept behind SVM. This book starts with explaining basic
knowledge of convex optimization and then introduces linear support
vector classification and regression. Next, it discusses kernel SVM
and the practical implementation. The setting allows readers to
easily learn how optimization techniques are used in a machine
learning technique such as SVM.
—Chih-Jen Lin, Professor, Department of Computer Science, National
Taiwan University
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