Band 240
Multistrategy Learning A Special Issue of MACHINE LEARNING
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- Hardcover
- Taschenbuch ausgewählt
- eBook
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Sprache:Englisch
Fr. 241.00
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
08.10.2012
Abbildungen
IV, 155 p.
Herausgeber
Ryszard S. MichalskiVerlag
Springer UsSeitenzahl
155
Maße (L/B/H)
23.5/15.5/1 cm
Gewicht
260 g
Auflage
Softcover reprint of the original 1st edition 1993
Sprache
Englisch
ISBN
978-1-4613-6405-4
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
m
ultistrategy 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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