9780792392774 - Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning (The Springer International Series in Engineering and Computer Science, 194, Band 194) (5 Ergebnisse)

Zustand: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Hardcover. Zustand: Very Good. Series: International Series in Engineering and Computer Science. x 339p hardback with laminated cover, very good copy from a Cambridge college library, library markings crossed out, a very good, unused copy Language: English.

Gebunden. Zustand: New. One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current co.

Zustand: New. Documents the progress of basic research in knowledge acquisition sponsored by the Office of Naval Research. This title includes summaries of work by cognitive scientists who do computational modeling of human learning. It also includes work in analogical reasoning; induction and discovery; explanation-based learning; and, more. Editor(s): Chipman, Susan F.; Meyrowitz, Alan L. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 339 pages, biography. BIC Classification: UYQM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 20. Weight in Grams: 1490. . 1992. Hardback. . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Neuware - One of the most intriguing questions about the new computer technology that has appeared over the past few decades is whether we humans will ever be able to make computers learn. As is painfully obvious to even the most casual computer user, most current computers do not. Yet if we could devise learning techniques that enable computers to routinely improve their performance through experience, the impact would be enormous. The result would be an explosion of new computer applications that would suddenly become economically feasible (e. g. , personalized computer assistants that automatically tune themselves to the needs of individual users), and a dramatic improvement in the quality of current computer applications (e. g. , imagine an airline scheduling program that improves its scheduling method based on analyzing past delays). And while the potential economic impact ofsuccessful learning methods is sufficient reason to invest in research into machine learning, there is a second significant reason: studying machine learning helps us understand our own human learning abilities and disabilities, leading to the possibility of improved methods in education. While many open questions remain aboutthe methods by which machines and humans might learn, significant progress has been made.