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Learning from data : Concepts, theory, and methods / by Vladimir Cherkassky and Filip Mulier

Por: Cherkassky, Vladimir S, autor.
Colaborador(es): Mulier, Filip, autor.
Tipo de material: materialTypeLabelE-bookHoboken, N.J. : IEEE Press , 2007Edición: 2nd ed 2007.Descripción: 1 recurso en línea.ISBN: 9780470140529; 9780470140512.Tema: Ingeniería del softwareRecursos en línea: Acceso a este recurso digital (usuarios Universidad Europea de Valencia)Digital Resources
Contenidos:
Problem statement, classical approaches, and adaptive learning -- Regularization framework -- Statistical learning theory -- Nonlinear optimization strategies -- Methods for data reduction and dimensionality reduction -- Methods for regression -- Classification -- Support vector machines -- Noninductive inference and alternative learning formulations
Resumen: An interdisciplinary framework for learning methodologies--covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied--showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science
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Existencias
Tipo de ítem Biblioteca actual Signatura topográfica Estado Fecha de vencimiento Código de barras Reserva de ítems
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Valencia Digital Acceso Electrónico (UEV) TK5102.9 2007 EB (Navegar estantería(Abre debajo)) Acceso electrónico
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Incluye índice y referencias bibliográficas (páginas 519-531)

Problem statement, classical approaches, and adaptive learning -- Regularization framework -- Statistical learning theory -- Nonlinear optimization strategies -- Methods for data reduction and dimensionality reduction -- Methods for regression -- Classification -- Support vector machines -- Noninductive inference and alternative learning formulations

An interdisciplinary framework for learning methodologies--covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied--showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science

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