Catálogo de publicaciones - libros
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
2006
Información sobre derechos de publicación
© Springer Science+Business Media, Inc. 2006
Tabla de contenidos
Introduction
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. 1-24
Inductive Genetic Programming
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. 25-54
Tree-Like PNN Representations
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. 55-80
Fitness Functions and Landscapes
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. 81-110
Search Navigation
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. 111-146
Backpropagation Techniques
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. 147-180
Temporal Backpropagation
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. 181-208
Bayesian Inference Techniques
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. 209-240
Statistical Model Diagnostics
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. 241-272
Time Series Modelling
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. 273-290