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Learning with Nested Generalized Exemplars (Paperback, Softcover reprint of the original 1st ed. 1990)
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Learning with Nested Generalized Exemplars (Paperback, Softcover reprint of the original 1st ed. 1990)
Series: The Springer International Series in Engineering and Computer Science, 100
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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."
General
Imprint: |
Springer-Verlag New York
|
Country of origin: |
United States |
Series: |
The Springer International Series in Engineering and Computer Science, 100 |
Release date: |
September 2011 |
First published: |
1990 |
Authors: |
Steven L. Salzberg
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Dimensions: |
235 x 155 x 10mm (L x W x T) |
Format: |
Paperback
|
Pages: |
160 |
Edition: |
Softcover reprint of the original 1st ed. 1990 |
ISBN-13: |
978-1-4612-8830-5 |
Categories: |
Books >
Computing & IT >
Applications of computing >
Artificial intelligence >
General
|
LSN: |
1-4612-8830-4 |
Barcode: |
9781461288305 |
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