Verlag: Kluwer Academic Pub, Norwell, Massachusetts, U.S.A., 1993
ISBN 10: 0792392787 ISBN 13: 9780792392781
Sprache: Englisch
Anbieter: Doss-Haus Books, Redondo Beach, CA, USA
Hardcover. Zustand: Good+. Hardcover 1993 edition. Ex-library book with stamps and labels attached. Binding firm. Pages unmarked and clean. Covers show minor wear to corners and edges. Text in good plus condition. {334 pages}.
Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher.
Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.
Taschenbuch. Zustand: Neu. Foundations of Knowledge Acquisition | Machine Learning | Alan L. Meyrowitz (u. a.) | Taschenbuch | XII | Englisch | 2013 | Springer | EAN 9781475783926 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
EUR 178,14
Anzahl: Mehr als 20 verfügbar
In den WarenkorbGebunden. 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.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 of successful 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 about the methods by which machines and humans might learn, significant progress has been made.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 234,36
Anzahl: 2 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. reprint edition. 345 pages. 9.26x6.11x0.80 inches. In Stock.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
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 of successful 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 about the methods by which machines and humans might learn, significant progress has been made.