Introduction to Computational Learning Theory

ISBN-10: 0262111934

ISBN-13: 9780262111935

Edition: 1994

List price: $70.00 Buy it from $56.77
This item qualifies for FREE shipping

*A minimum purchase of $35 is required. Shipping is provided via FedEx SmartPost® and FedEx Express Saver®. Average delivery time is 1 – 5 business days, but is not guaranteed in that timeframe. Also allow 1 - 2 days for processing. Free shipping is eligible only in the continental United States and excludes Hawaii, Alaska and Puerto Rico. FedEx service marks used by permission."Marketplace" orders are not eligible for free or discounted shipping.

30 day, 100% satisfaction guarantee

If an item you ordered from TextbookRush does not meet your expectations due to an error on our part, simply fill out a return request and then return it by mail within 30 days of ordering it for a full refund of item cost.

Learn more about our returns policy

Description:

Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.
New Starting from $80.65
what's this?
Rush Rewards U
Members Receive:
coins
coins
You have reached 400 XP and carrot coins. That is the daily max!
Study Briefs

Limited time offer: Get the first one free! (?)

All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.

Add to cart
Study Briefs
SQL Online content $4.95 $1.99
Add to cart
Study Briefs
MS Excel® 2010 Online content $4.95 $1.99
Add to cart
Study Briefs
MS Word® 2010 Online content $4.95 $1.99
Add to cart
Study Briefs
MS PowerPoint® 2010 Online content $4.95 $1.99
Customers also bought
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading

Book details

List price: $70.00
Copyright year: 1994
Publisher: MIT Press
Publication date: 8/15/1994
Binding: Hardcover
Pages: 221
Size: 7.25" wide x 9.50" long x 0.75" tall
Weight: 1.232
Language: English

Michael J. Kearns is Professor of Computer and Information Science at the University of Pennsylvania.

Preface
The Probably Approximately Correct Learning Model
Occam's Razor
The Vapnik-Chervonenkis Dimension
Weak and Strong Learning
Learning in the Presence of Noise
Inherent Unpredictability
Reducibility in PAC Learning
Learning Finite Automata by Experimentation
Appendix: Some Tools for Probabilistic Analysis
Bibliography
Index
×
Free shipping on orders over $35*

*A minimum purchase of $35 is required. Shipping is provided via FedEx SmartPost® and FedEx Express Saver®. Average delivery time is 1 – 5 business days, but is not guaranteed in that timeframe. Also allow 1 - 2 days for processing. Free shipping is eligible only in the continental United States and excludes Hawaii, Alaska and Puerto Rico. FedEx service marks used by permission."Marketplace" orders are not eligible for free or discounted shipping.

Learn more about the TextbookRush Marketplace.

×