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

• Autore: Cherkassky Vladimir S
• Pubblicazione: Hoboken, New Jersey : , : IEEE Press : , c2007
• Edizione: 2nd ed.
• Descrizione fisica: 1 online resource (558 p.)
• Disciplina: 006.31; 006.32
• Soggetto topico: Adaptive signal processing; Machine learning; Neural networks (Computer science); Fuzzy systems
• Altri autori: MulierFilip
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references (p. 519-531) and index.
• Nota di contenuto: 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.
• Sommario/riassunto: 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. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.
• Titolo autorizzato: Learning from data
• ISBN: 1-281-00188-0; 9786611001889; 0-470-14052-6; 0-470-14051-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996215456703316
• Lo trovi qui: Univ. di Salerno