Michael J. Kearns

Umesh Vazirani

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. 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.

An Introduction to Computational Learning Theory

The dragon lord to the five kingdoms has been assassinated, throwing the kingdoms into turmoil. After young dragon rider Alaric Smithson is relieved of duty, he enters to vie for the position of dragon lord. With his reluctant companion, the seer Cassandra Youngblood, they uncover a conspiracy that could disrupt the Spring Council of the five kingdoms and bring about war. Together with Alaric’s father, Adric Smithson, captain of the Palace Guard, and Princess Kari, Cassandra’s mentor, they race against time to expose the conspirators to prevent the war that is about to be thrust upon them.

Conspiracy of Kingdoms

Dragon Lord Chronicles

Michael I. Jordan

Sara A. Solla

The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. These proceedings contain all of the papers that were presented.

Michael J. Kearns

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