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An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods
This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.
204 pages, Kindle Edition
First published March 23, 2000
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Displaying 1 - 5 of 5 reviews
November 3, 2012
Every now and then, you come across a mathematical concept which astonishes you by turning something you've always intuitively known into a precise formula. Support Vector Machines do a remarkably good job of capturing the way in which we learn to distinguish between two kinds of objects. Let's call them Good and Bad, and let's suppose we're given a bunch of examples of Good and Bad objects. We want to be able to use our data as efficiently as we can.
Simplifying a fair amount, our goal is to find a rule which will separate the Good examples from the Bad ones. Now what will a good rule look like? We have two competing intuitions. On the one hand, for obvious reasons, we want our rule to account for as many of the examples as possible. But on the other, we also want our rule to be as simple as possible. If the rule is too complicated, we intuitively feel that it won't be general, and we may not have learned anything useful. Impressive rules are generally impressive because they're straightforward.
Well: it all sounds very hand-wavy... but, in fact, you can express these ideas in precise terms and realise them as an easy-to-use piece of software. Cristianini's and Shawe-Taylor's book does a fine job of walking you through the details. If you have a reasonable amount of mathematical background, strongly recommended.
Simplifying a fair amount, our goal is to find a rule which will separate the Good examples from the Bad ones. Now what will a good rule look like? We have two competing intuitions. On the one hand, for obvious reasons, we want our rule to account for as many of the examples as possible. But on the other, we also want our rule to be as simple as possible. If the rule is too complicated, we intuitively feel that it won't be general, and we may not have learned anything useful. Impressive rules are generally impressive because they're straightforward.
Well: it all sounds very hand-wavy... but, in fact, you can express these ideas in precise terms and realise them as an easy-to-use piece of software. Cristianini's and Shawe-Taylor's book does a fine job of walking you through the details. If you have a reasonable amount of mathematical background, strongly recommended.
April 4, 2023
"An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods" is an excellent resource for anyone interested in understanding kernel-based learning methods.
The first two chapters provide a solid general introduction to machine learning, with a focus on linear learning methods. The third chapter is particularly noteworthy, as it provides an excellent overview of kernels and kernel-based learning, making it a must-read for anyone interested in these methods.
Chapters 4 and 5 focus on generalization theory and optimization theory, respectively, and are the most challenging to follow in the book. These chapters require a strong background in mathematics to fully grasp the concepts presented.
Chapter 6 is the core of the book, as it introduces the reader to Support Vector Machines (SVMs). This chapter is well-written and accessible, providing a solid understanding of the theory behind SVMs.
The remaining chapters are focused on implementation and are more technical in nature than mathematical.
The first two chapters provide a solid general introduction to machine learning, with a focus on linear learning methods. The third chapter is particularly noteworthy, as it provides an excellent overview of kernels and kernel-based learning, making it a must-read for anyone interested in these methods.
Chapters 4 and 5 focus on generalization theory and optimization theory, respectively, and are the most challenging to follow in the book. These chapters require a strong background in mathematics to fully grasp the concepts presented.
Chapter 6 is the core of the book, as it introduces the reader to Support Vector Machines (SVMs). This chapter is well-written and accessible, providing a solid understanding of the theory behind SVMs.
The remaining chapters are focused on implementation and are more technical in nature than mathematical.
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March 23, 2021
This text is about as pedagogical as the back of a 1040 tax form. If you don't already know what a reproducing kernel Hilbert space is, then this book will be about as helpful as a bag of hammers. It's like a bunch of support vector machines invite you out to play then just pull your undies over your head and drop you in a dumpster.
Displaying 1 - 5 of 5 reviews