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Foundations of Knowledge Acquisition: Machine Learning (The Springer International Series in Engineering and Computer Science)
Foundations of Knowledge Acquisition: Machine Learning (The Springer International Series in Engineering and Computer Science)

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Manufacturer: Kluwer Academic Publishers
Publisher: Kluwer Academic Publishers

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Product Description:
Binding: Hardcover
Brand: Brand: Kluwer Academic Publishers
EAN: 9780792392781
Edition: 1993
Feature: Used Book in Good Condition
ISBN: 0792392787
Item Dimensions: Array
Label: Kluwer Academic Publishers
Languages: Array
Manufacturer: Kluwer Academic Publishers
Number Of Items: 1
Number Of Pages: 334
Publication Date: 1993-01-31
Publisher: Kluwer Academic Publishers
Studio: Kluwer Academic Publishers
Product Features:
Used Book in Good Condition
Editorial Review:
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.
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