Multistrategy Learning

Name: Multistrategy Learning
Brand: Springer, Berlin
SKU: 9781461364054
Price: 950.44 PLN
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A Special Issue of MACHINE LEARNING

Autor: Ryszard S. Michalski

Język:

Oprawa: Miękka

Wydawca: Springer, Berlin

Dostępność: Dostępna u dostawcy w małych ilościach

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Most machine learning research has been concerned with the development of systems that implememnt on...

Informacje o książce

Autor

Ryszard S. Michalski

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2012

strony

155

EAN

9781461364054

ISBN

1461364051

Enbook ID

02076575

Wydawca

Springer, Berlin

Waga

260

Wymiary

155 x 235 x 9

Pełny opis

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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Multistrategy Learning

A Special Issue of MACHINE LEARNING

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