Learning from data : concepts, theory, and methods / Vladimir Cherkassky, Filip Mulier.
By: Cherkassky, Vladimir S [author.]
.
Contributor(s): Mulier, Filip | IEEE Xplore (Online service) [distributor.].
Material type: 
BookPublisher: Hoboken, New Jersey : IEEE Press : c2007Edition: 2nd ed.Description: 1 PDF (xviii, 538 pages) : illustrations.Content type: text Media type: electronic Carrier type: online resourceISBN: 9780470140529.Subject(s): Adaptive signal processing | Machine learning | Neural networks (Computer science) | Fuzzy systems | Adaptation model | Aerospace electronics | Analytical models | Approximation algorithms | Approximation methods | Artificial intelligence | Artificial neural networks | Bibliographies | Biological system modeling | Biology | Books | Boosting | Clustering algorithms | Clustering methods | Complexity theory | Convergence | Data models | Dictionaries | Eigenvalues and eigenfunctions | Encoding | Estimation | Function approximation | Generators | Hafnium | Humans | Hypercubes | Indexes | Iterative methods | Kernel | Learning systems | Linear approximation | Machine learning | Matrix decomposition | Minimization | Newton method | Optimization | Optimization methods | Parameter estimation | Pattern recognition | Polynomials | Predictive models | Principal component analysis | Probabilistic logic | Probability | Prototypes | Risk management | Sections | Singular value decomposition | Statistical learning | Support vector machines | Symmetric matrices | Taxonomy | Training | Training data | Uncertainty | Unsupervised learning | Vector quantization | Vectors | ZincGenre/Form: Electronic books.Additional physical formats: Print version:: No titleDDC classification: 006.3/1 Online resources: Abstract with links to resource Also available in print.Includes bibliographical references (p. 519-531) and index.
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.
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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.
Also available in print.
Electronic reproduction. Piscataway, N.J. : IEEE, 2010. Mode of access: World Wide Web. System requirements: Web browser. Title from title screen (viewed on Oct. 7, 2010). Access may be restricted to users at subscribing institutions.
Mode of access: World Wide Web.
Description based on PDF viewed 12/19/2015.
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