Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
FREE Two-Day Shipping for Students. Learn more |
 Sell Back Your Copy for $2.87
Whether you buy it used on Amazon for $19.00 or somewhere else, you can sell it back through our Book Trade-In Program at the current price of $2.87.
Used Price$19.00
Trade-in Price$2.87
Price after
Trade-in$16.13 |
Book Description
Special Offers and Product Promotions
- Buy $50 in qualifying physical textbooks, get $5 in Amazon MP3 Credit. Here's how (restrictions apply)
Frequently Bought Together
Editorial Reviews
About the Author
Ralf Herbrich is a Postdoctoral Researcher in the Machine Learning and Perception Group at Microsoft Research Cambridge and a Research Fellow of Darwin College, University of Cambridge.
Product Details
Would you like to update product info or give feedback on images?
|
More About the Author
Discover books, learn about writers, read author blogs, and more.
Customer Reviews
|
Share your thoughts with other customers:
|
||||||||||||||||||||||
|
Most Helpful Customer Reviews
2 of 11 people found the following review helpful:
5.0 out of 5 stars
Fascinating,
By A Customer
This review is from: Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning) (Hardcover)
An fine introduction to statistical learning theory! While the audio book format is certainly an unorthodox choice, the breathy, Jessica Rabbit-style narration turns out to be a boon when getting to grips with algorithmic stability and PAC bounds. First rate!
Share your thoughts with other customers: Create your own review
|
|
Inside This Book
(learn more)
First Sentence:
It was only a few years after the introduction of the first computer that one of man's greatest dreams seemed to be realizable-artificial intelligence. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
probable smoothness, inscribable ball, empirical risk minimization algorithm, fixed learning algorithm, soft margin loss, geometrical margin, risk minimization algorithms, soft margin support vector machines, generalization error bound, support vector algorithm, reversal lemma, fat shattering dimension, ghost sample, compression framework, functional margin, vector machine algorithm, zero training error, margin machines, perceptron learning algorithm, kernel classifiers, relevance vector machines, drawn iid, training sample size, algorithmic stability, hypothesis space New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
It was only a few years after the introduction of the first computer that one of man's greatest dreams seemed to be realizable-artificial intelligence. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
probable smoothness, inscribable ball, empirical risk minimization algorithm, fixed learning algorithm, soft margin loss, geometrical margin, risk minimization algorithms, soft margin support vector machines, generalization error bound, support vector algorithm, reversal lemma, fat shattering dimension, ghost sample, compression framework, functional margin, vector machine algorithm, zero training error, margin machines, perceptron learning algorithm, kernel classifiers, relevance vector machines, drawn iid, training sample size, algorithmic stability, hypothesis space New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
Citations (learn more)
This book cites 36
books:
See all 36 books this book cites
6
books
cite this book:
See all 6 books citing this book
- Neural Networks and Machine Learning (NATO ASI Series / Computer and Systems Sciences) by Christopher M. Bishop in Back Matter (1), and Back Matter (2)
- Graphical Models for Machine Learning and Digital Communication (Adaptive Computation and Machine Learning) by Brendan J. Frey in Front Matter
- Learning in Graphical Models (Adaptive Computation and Machine Learning) by Michael I. Jordan in Front Matter
- An Introduction to Computational Learning Theory by Michael J. Kearns in Back Matter
- Principles of Data Mining (Adaptive Computation and Machine Learning) by David J. Hand in Front Matter
See all 36 books this book cites
- Advanced Lectures on Machine Learning: Machine Learning Summer School 2002, Canberra, Australia, February 11-22, 2002, Revised Lectures (Lecture Notes in Computer Science) by Shahar Mendelson on page 114, and Back Matter
- Kernel Methods for Pattern Analysis by John Shawe-Taylor in Back Matter
- KI 2005: Advances in Artificial Intelligence: 28th Annual German Conference on AI, KI 2005, Koblenz, Germany, September 11-14, 2005, Proceedings ... / Lecture Notes in Artificial Intelligence) by Ulrich Furbach on page 119
- Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series) by Carl Edward Rasmussen in Front Matter
- All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics) by Larry Wasserman in Back Matter
See all 6 books citing this book
Books on Related Topics
(learn more)
|
What Other Items Do Customers Buy After Viewing This Item?
Tags Customers Associate with This Product(What's this?)Click on a tag to find related items, discussions, and people.
|
Sell a Digital Version of This Book in the Kindle Store
If you are a publisher or author and hold the digital rights to a book, you can sell a digital version of it in our Kindle Store.
Learn more
Customer Discussions
|
This product's forum
Active discussions in related forums
Search Customer Discussions
|
Related forums
|
