Learning with Nested Generalized Exemplars (The Springer International Series ..

The Book Bin
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“159 pp. Minor rubbing on cover. Tight binding, clean copy. Size: 8vo - over 7 3/4 in - 9 3/4 in ”... Read moreabout condition
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Item specifics

Condition
Very Good
A book that does not look new and has been read but is in excellent condition. No obvious damage to the cover, with the dust jacket (if applicable) included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear. See the seller’s listing for full details and description of any imperfections. See all condition definitionsopens in a new window or tab
Seller Notes
“159 pp. Minor rubbing on cover. Tight binding, clean copy. Size: 8vo - over 7 3/4 in - 9 3/4 in ...
ISBN
9780792391104
Subject Area
Computers, Education
Publication Name
Learning with Nested Generalized Exemplars
Publisher
Springer
Item Length
9.3 in
Subject
Intelligence (Ai) & Semantics, Learning Styles
Publication Year
1990
Series
The Springer International Series in Engineering and Computer Science Ser.
Type
Textbook
Format
Hardcover
Language
English
Author
Steven L. Salzberg
Item Weight
34.2 Oz
Item Width
6.1 in
Number of Pages
Xx, 160 Pages
Category

About this product

Product Identifiers

Publisher
Springer
ISBN-10
0792391101
ISBN-13
9780792391104
eBay Product ID (ePID)
960221

Product Key Features

Number of Pages
Xx, 160 Pages
Publication Name
Learning with Nested Generalized Exemplars
Language
English
Subject
Intelligence (Ai) & Semantics, Learning Styles
Publication Year
1990
Type
Textbook
Subject Area
Computers, Education
Author
Steven L. Salzberg
Series
The Springer International Series in Engineering and Computer Science Ser.
Format
Hardcover

Dimensions

Item Weight
34.2 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
90-034231
Dewey Edition
20
Series Volume Number
100
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
006.3
Table Of Content
1 Introduction.- 1.1 Background.- 1.2 NGE and other exemplar-based theories.- 1.3 Previous models.- 1.4 Comparisons of NGE and other models.- 1.5 Types of generalization.- 2 The NGE learning algorithm.- 2.1 Initialization.- 2.2 Get the next example.- 2.3 Make a prediction.- 2.4 Feedback.- 2.5 Summary of algorithm.- 2.6 Partitioning feature space.- 2.7 Assumptions.- 2.8 Greedy variant of the algorithm.- 3 Review.- 3.1 Concept learning in psychology.- 3.2 Prototype theory and exemplar theory.- 3.3 Each as a multiple prototype model.- 3.4 Machine learning in AI.- 3.5 Connectionism.- 3.6 Cluster analysis.- 3.7 Conclusion.- 4 Experimental results with NGE.- 4.1 Breast cancer data.- 4.2 Iris classification.- 4.3 Echocardiogram tests.- 4.4 Discrete event simulation.- 5 Conclusion.- 5.1 Weight factors.- 5.2 Synthesis with explanation-based learning.- 5.3 Psychological plausibility.- 5.4 Complexity results.- 5.5 Future experimental work.- A Data sets.- A.1 Breast cancer data.- A.2 Iris data.- A.3 Echocardiogram data.
Synopsis
Machine Learning is one of the oldest and most intriguing areas of Ar­ tificial Intelligence. From the moment that computer visionaries first began to conceive the potential for general-purpose symbolic computa­ tion, the concept of a machine that could learn by itself has been an ever present goal. Today, although there have been many implemented com­ puter programs that can be said to learn, we are still far from achieving the lofty visions of self-organizing automata that spring to mind when we think of machine learning. We have established some base camps and scaled some of the foothills of this epic intellectual adventure, but we are still far from the lofty peaks that the imagination conjures up. Nevertheless, a solid foundation of theory and technique has begun to develop around a variety of specialized learning tasks. Such tasks in­ clude discovery of optimal or effective parameter settings for controlling processes, automatic acquisition or refinement of rules for controlling behavior in rule-driven systems, and automatic classification and di­ agnosis of items on the basis of their features. Contributions include algorithms for optimal parameter estimation, feedback and adaptation algorithms, strategies for credit/blame assignment, techniques for rule and category acquisition, theoretical results dealing with learnability of various classes by formal automata, and empirical investigations of the abilities of many different learning algorithms in a diversity of applica­ tion areas., Machine Learning is one of the oldest and most intriguing areas of Ar- tificial Intelligence. From the moment that computer visionaries first began to conceive the potential for general-purpose symbolic computa- tion, the concept of a machine that could learn by itself has been an ever present goal. Today, although there have been many implemented com- puter programs that can be said to learn, we are still far from achieving the lofty visions of self-organizing automata that spring to mind when we think of machine learning. We have established some base camps and scaled some of the foothills of this epic intellectual adventure, but we are still far from the lofty peaks that the imagination conjures up. Nevertheless, a solid foundation of theory and technique has begun to develop around a variety of specialized learning tasks. Such tasks in- clude discovery of optimal or effective parameter settings for controlling processes, automatic acquisition or refinement of rules for controlling behavior in rule-driven systems, and automatic classification and di- agnosis of items on the basis of their features. Contributions include algorithms for optimal parameter estimation, feedback and adaptation algorithms, strategies for credit/blame assignment, techniques for rule and category acquisition, theoretical results dealing with learnability of various classes by formal automata, and empirical investigations of the abilities of many different learning algorithms in a diversity of applica- tion areas.
LC Classification Number
Q334-342

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The Book Bin

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Joined Nov 1998
The Book Bin is a family run bookstore that has been in business for over thirty years and has three locations, one in Corvallis OR and two in Salem,OR. We are long time members of the ABAA ...
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