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Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods

Nikolay Y. Nikolaev Hitoshi Iba

Resumen/Descripción – provisto por la editorial

No disponible.

Palabras clave – provistas por la editorial

Theory of Computation; Artificial Intelligence (incl. Robotics); Computing Methodologies

Disponibilidad
Institución detectada Año de publicación Navegá Descargá Solicitá
No detectada 2006 SpringerLink

Información

Tipo de recurso:

libros

ISBN impreso

978-0-387-31239-2

ISBN electrónico

978-0-387-31240-8

Editor responsable

Springer Nature

País de edición

Reino Unido

Fecha de publicación

Información sobre derechos de publicación

© Springer Science+Business Media, Inc. 2006

Tabla de contenidos

Conclusions

Nikolay Y. Nikolaev; Hitoshi Iba

We show by way of example how one can provide in a lot of cases simple modular semantics of rules of inference, so that the semantics of a system is obtained by joining the semantics of its rules in the most straightforward way. Our main tool for this task is the use of finite Nmatrices, which are multi-valued structures in which the value assigned by a valuation to a complex formula can be chosen non-deterministically out of a certain nonempty set of options. The method is applied in the area of logics with a formal consistency operator (knowns as LFIs), allowing us to provide in a modular way effective, finite semantics for thousands of different LFIs.

Pp. 291-294