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.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Michael J. Kearns is Professor of Computer and Information Science at the University of Pennsylvania.
Umesh Vazirani is Roger A. Strauch Professor in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
Anbieter: thebookforest.com, San Rafael, CA, USA
Zustand: Good. hardcover. Page block firm and clean, binding unblemished, boards straight, without markings of any kind. Black cloth without DJ. Mild shelf wear. Supporting Bay Area Friends of the Library since 2010. Well packaged and promptly shipped. Bestandsnummer des Verkäufers BAY19-00218
Anzahl: 1 verfügbar
Anbieter: WorldofBooks, Goring-By-Sea, WS, Vereinigtes Königreich
Hardback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged. Bestandsnummer des Verkäufers GOR015015825
Anzahl: 1 verfügbar
Anbieter: YESIBOOKSTORE, MIAMI, FL, USA
hardcover. Zustand: As New. Bestandsnummer des Verkäufers 0262111934-VB
Anzahl: 1 verfügbar
Anbieter: BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, Deutschland
Zustand: gut. 1994. An Introduction to Computational Learning Theory (The MIT Press) In englischer Sprache. pages. Bestandsnummer des Verkäufers BN342414
Anzahl: 1 verfügbar
Anbieter: GoldBooks, Denver, CO, USA
Hardcover. Zustand: new. New Copy. Customer Service Guaranteed. Bestandsnummer des Verkäufers 61F78_86_0262111934
Anzahl: 1 verfügbar