Description: Multistrategy Learning : A Special Issue of Machine Learning, Paperback by Michalski, Ryszard S. (EDT), ISBN 1461364051, ISBN-13 9781461364054, Brand New, Free shipping in the US 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.
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Book Title: Multistrategy Learning : A Special Issue of Machine Learning
Number of Pages: IV, 155 Pages
Publication Name: Multistrategy Learning : a Special Issue of Machine Learning
Language: English
Publisher: Springer
Publication Year: 2012
Subject: Intelligence (Ai) & Semantics, General
Type: Textbook
Item Weight: 9.2 Oz
Subject Area: Computers
Item Length: 9.3 in
Author: Ryszard S. Michalski
Item Width: 6.1 in
Series: The Springer International Series in Engineering and Computer Science Ser.
Format: Trade Paperback
