Description: Multistrategy Learning Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). A Special Issue of MACHINE LEARNING Author(s): Ryszard S. Michalski Format: Paperback Publisher: Springer-Verlag New York Inc., United States Imprint: Springer-Verlag New York Inc. ISBN-13: 9781461364054, 978-1461364054 Synopsis 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
Number of Pages: 155 Pages
Publication Name: Multistrategy Learning: a Special Issue of Machine Learning
Language: English
Publisher: Springer-Verlag New York Inc.
Item Height: 235 mm
Subject: Computer Science
Publication Year: 2012
Type: Textbook
Item Weight: 260 g
Author: Ryszard S. Michalski
Item Width: 155 mm
Series: The Springer International Series in Engineering and Computer Science
Format: Paperback