Multistrategy Learning : a Special Issue of MACHINE LEARNING /
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, explan...
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| Format: | eBook |
| Language: | English |
| Published: |
Boston, MA :
Springer US,
1993.
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| Series: | Springer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems ;
240. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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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| Item Description: | Electronic resource. |
| Physical Description: | 1 online resource (iv, 155 pages) |
| ISBN: | 9781461532026 (electronic bk.) 1461532027 (electronic bk.) |
| ISSN: | 0893-3405 ; |