Knowledge Processing with Interval and Soft Computing / edited by Vladik Kreinovich, Andre Korvin, R. Baker Kearfott, Chenyi Hu.

Por:

Kreinovich, Vladik [editor.]

Colaborador(es):

Tipo de material: Texto

TextoSeries Advanced Information and Knowledge ProcessingEditor: London : Springer London, 2008Descripción: recurso en líneaTipo de contenido:

texto

Tipo de medio:

computadora

Tipo de portador:

recurso en línea

ISBN:

9781848003262

Formatos físicos adicionales: Edición impresa:: Sin títuloRecursos en línea:

Conectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)

Contenidos:

Fundamentals of Interval Computing -- Soft Computing Essentials -- Relations Between Interval Computing and Soft Computing -- Interval Matrices in Knowledge Discovery -- Interval Function Approximation and Applications -- Interval Rule Matrices for Decision Making -- Interval Matrix Games -- Interval-Weighted Graphs and Flow Networks -- Arithmetic on Bounded Families of Distributions A Denv Algorithm Tutorial -- IntBox An Object-Oriented Interval Computing Software Toolbox in C++.

Resumen: Massive datasets, made available today by modern technologies, present a significant challenge to scientists who need to effectively and efficiently extract relevant knowledge and information. Due to their ability to model uncertainty, interval and soft computing techniques have been found to be effective in this extraction. This book provides coverage of the basic theoretical foundations for applying these techniques to artificial intelligence and knowledge processing. The first three chapters provide the background needed for those who are unfamiliar with interval and soft computing techniques. The following chapters describe innovative algorithms and their applications to knowledge processing. In particular, these chapters cover computing techniques for interval linear systems of equations, interval matrix singular-value decomposition, interval function approximation, and decision making with statistical and graph-based data processing. To enable these applications, the book presents a standards-based object-oriented interval computing environment in C++. By providing the necessary background and summarizing recent results and successful applications, this self-contained book will serve as a useful resource for researchers and practitioners wanting to learn interval and soft computing techniques and apply them to artificial intelligence and knowledge processing.

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Springer eBooks

Massive datasets, made available today by modern technologies, present a significant challenge to scientists who need to effectively and efficiently extract relevant knowledge and information. Due to their ability to model uncertainty, interval and soft computing techniques have been found to be effective in this extraction. This book provides coverage of the basic theoretical foundations for applying these techniques to artificial intelligence and knowledge processing. The first three chapters provide the background needed for those who are unfamiliar with interval and soft computing techniques. The following chapters describe innovative algorithms and their applications to knowledge processing. In particular, these chapters cover computing techniques for interval linear systems of equations, interval matrix singular-value decomposition, interval function approximation, and decision making with statistical and graph-based data processing. To enable these applications, the book presents a standards-based object-oriented interval computing environment in C++. By providing the necessary background and summarizing recent results and successful applications, this self-contained book will serve as a useful resource for researchers and practitioners wanting to learn interval and soft computing techniques and apply them to artificial intelligence and knowledge processing.

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