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...
Gespeichert in:
| Weitere Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1993
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| Schriftenreihe: | The Springer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems
240 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (IV, 155 p) |
| ISBN: | 9781461532026 |
| DOI: | 10.1007/978-1-4615-3202-6 |
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| 520 | |a 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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Datensatz im Suchindex
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| id | DE-604.BV045186467 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:57Z |
| institution | BVB |
| isbn | 9781461532026 |
| language | English |
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| publishDate | 1993 |
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| series2 | The Springer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems |
| spelling | Multistrategy Learning A Special Issue of MACHINE LEARNING edited by Ryszard S. Michalski Boston, MA Springer US 1993 1 Online-Ressource (IV, 155 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science, Knowledge Representation, Learning and Expert Systems 240 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 Computer Science Artificial Intelligence (incl. Robotics) Computer Science, general Computer science Artificial intelligence Michalski, Ryszard S. 1937-2007 (DE-588)11030196X edt Erscheint auch als Druck-Ausgabe 9781461364054 https://doi.org/10.1007/978-1-4615-3202-6 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Multistrategy Learning A Special Issue of MACHINE LEARNING Computer Science Artificial Intelligence (incl. Robotics) Computer Science, general Computer science Artificial intelligence |
| title | Multistrategy Learning A Special Issue of MACHINE LEARNING |
| title_auth | Multistrategy Learning A Special Issue of MACHINE LEARNING |
| title_exact_search | Multistrategy Learning A Special Issue of MACHINE LEARNING |
| title_full | Multistrategy Learning A Special Issue of MACHINE LEARNING edited by Ryszard S. Michalski |
| title_fullStr | Multistrategy Learning A Special Issue of MACHINE LEARNING edited by Ryszard S. Michalski |
| title_full_unstemmed | Multistrategy Learning A Special Issue of MACHINE LEARNING edited by Ryszard S. Michalski |
| title_short | Multistrategy Learning |
| title_sort | multistrategy learning a special issue of machine learning |
| title_sub | A Special Issue of MACHINE LEARNING |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Computer Science, general Computer science Artificial intelligence |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Computer Science, general Computer science Artificial intelligence |
| url | https://doi.org/10.1007/978-1-4615-3202-6 |
| work_keys_str_mv | AT michalskiryszards multistrategylearningaspecialissueofmachinelearning |