Overview

Most machine learning research has been concerned with the development of systems that implememnt one type of inference within a single representational paradigm. Such systems, which can be called monostrategy learning systems, include those for empirical induction of decision trees or rules, explanation-based generalization, neural net learning from examples, genetic algorithm-based learning, and others. Monostrategy learning systems can be very effective and useful if learning problems to which they are applied are sufficiently narrowly defined. Many real-world applications, however, pose learning problems that go beyond the capability of monostrategy learning methods. In view of this, recent years have witnessed a growing interest in developing multistrategy systems, which integrate two or more inference types and/or paradigms within one learning system. Such multistrategy systems take advantage of the complementarity of different inference types or representational mechanisms. Therefore, they have a potential to be more versatile and more powerful than monostrategy systems. On the other hand, due to their greater complexity, their development is significantly more difficult and represents a new great challenge to the machine learning community. Multistrategy Learning contains contributions characteristic of the current research in this area.
ISBN-13

9781461364054

ISBN-10

1461364051

Weight

0.53 Pounds

Dimensions

6.10 x 0.37 x 9.25 In

List Price

$219.99

Edition

1st Edition

Format

Paperback

Language

English

Pages

iv, 155 pages

Publisher

Springer

Published On

2012-10-08


View All Offers

Sort by:

Condition
Seller
Seller Comments
Price
✨ Brand New
Seller details
Alibris
★★★★★

Sparks, NV, USA

Print on demand Trade paperback (US). Glued binding. 155 p. The Springer International Engineering ...
$221.48

 Free delivery by: 03 Apr 2026


Bookstores.com relies on cookies to improve your experience.