Foundations of Knowledge Acquisition | Machine Learning

Alan L. Meyrowitz (u. a.)

ISBN 10: 1475783922 / ISBN 13: 9781475783926

Published by Springer, 2013

About this Item

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 Print on Demand.

Seller Inventory # 105580537

Bibliographic Details

• Title: Foundations of Knowledge Acquisition | Machine Learning
• Publisher: Springer
• Publication Date: 2013
• Language: English
• ISBN 10: 1475783922
• ISBN 13: 9781475783926
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 20D millimeters 235 millimeters width by 155 millimeters height by 20 millimeters depth
• Weight: 534 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.