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An easy-to-follow introduction to support vector machines
This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover:
-
Knowledge discovery environments
-
Describing data mathematically
-
Linear decision surfaces and functions
-
Perceptron learning
-
Maximum margin classifiers
-
Support vector machines
-
Elements of statistical learning theory
-
Multi-class classification
-
Regression with support vector machines
-
Novelty detection
Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.
- Sales Rank: #1049854 in eBooks
- Published on: 2011-09-21
- Released on: 2011-09-21
- Format: Kindle eBook
From the Back Cover
An easy-to-follow introduction to support vector machines
This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover:
Knowledge discovery environments
Describing data mathematically
Linear decision surfaces and functions
Perceptron learning
Maximum margin classifiers
Support vector machines
Elements of statistical learning theory
Multi-class classification
Regression with support vector machines
Novelty detection
Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.
About the Author
Lutz Hamel, PhD, teaches at the University of Rhode Island, where he founded the machine learning and data mining group. His major research interests are computational logic, machine learning, evolutionary computation, data mining, bioinformatics, and computational structures in art and literature.
Most helpful customer reviews
7 of 8 people found the following review helpful.
An excellent and thorough introduction to Support Vector Machines
By Pankaj L. Ahire
If you need to understand Support Vector Machines from ground up, this is the book to get.
Every aspect of SVMs is discussed thoroughly in a very lucid manner.
The book does not lose itself in math, and at the same time it does not dumb it down - the theoretical rigor has just the right touch.
In addition, the book illustrates its concepts using R and Weka - both of which are excellent environments to get a first-hand feel of what is going on. Numerous examples in the two environment give a solid practical feel of the subject.
Overall, an excellent book on the subject and a constant companion if you use deal with Machine learning or use SVMs regularly.
3 of 3 people found the following review helpful.
Excellent introduction
By Sapere Aude
I started out with the Cristianini and Shawe-Taylor book to learn about Support Vector Machines. I had such a hard time digesting it, I went out looking for something better and found this book. At the time it had no reviews, but I found the author's website and saw he taught a course on SVM with this book so I took a chance. I was very pleased, this is an excellent introduction to SVMs. It doesn't assume you are already perfectly versed in optimization theory, he motivates all the mathematics you need to understand what he is talking about. After reading his book I was then able to digest the other material on the subject. Very well done.
5 of 6 people found the following review helpful.
Excellent Introduction - Very Approachable
By JMA
This is a fantastic book to learn the underlying mathematics, as well as the practicals, of various flavors of SVM. Dr. Hamel has produced a carefully crafted book that builds up slowly (but is well paced) providing all the required background within the subject treatment.
Based on the careful and clear explanations presented, it is apparent that Dr. Hamel is an excellent teacher, taking the time to really help students understand the material.
Highly recommended!
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