Ryszard S. Michalski 
Multistrategy Learning [PDF ebook] 
A Special Issue of MACHINE LEARNING

Ủng hộ
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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Ngôn ngữ Anh ● định dạng PDF ● ISBN 9781461532026 ● Biên tập viên Ryszard S. Michalski ● Nhà xuất bản Springer US ● Được phát hành 2012 ● Có thể tải xuống 3 lần ● Tiền tệ EUR ● TÔI 4613877 ● Sao chép bảo vệ Adobe DRM
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