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Produktbild: Feature Extraction
Band 207

Feature Extraction Foundations and Applications

Aus der Reihe Studies in Fuzziness and Soft Computing

291,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

30.04.2017

Herausgeber

Isabelle Guyon + weitere

Verlag

Springer Berlin

Seitenzahl

778

Maße (L/B/H)

23,5/15,5/4,3 cm

Gewicht

1194 g

Auflage

Softcover reprint of the original 1st edition 2006

Sprache

Englisch

ISBN

978-3-662-51771-0

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

30.04.2017

Herausgeber

Verlag

Springer Berlin

Seitenzahl

778

Maße (L/B/H)

23,5/15,5/4,3 cm

Gewicht

1194 g

Auflage

Softcover reprint of the original 1st edition 2006

Sprache

Englisch

ISBN

978-3-662-51771-0

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Feature Extraction
  • An Introduction to Feature Extraction.- An Introduction to Feature Extraction.- Feature Extraction Fundamentals.- Learning Machines.- Assessment Methods.- Filter Methods.- Search Strategies.- Embedded Methods.- Information-Theoretic Methods.- Ensemble Learning.- Fuzzy Neural Networks.- Feature Selection Challenge.- Design and Analysis of the NIPS2003 Challenge.- High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees.- Ensembles of Regularized Least Squares Classifiers for High-Dimensional Problems.- Combining SVMs with Various Feature Selection Strategies.- Feature Selection with Transductive Support Vector Machines.- Variable Selection using Correlation and Single Variable Classifier Methods: Applications.- Tree-Based Ensembles with Dynamic Soft Feature Selection.- Sparse, Flexible and Efficient Modeling using L 1 Regularization.- Margin Based Feature Selection and Infogain with Standard Classifiers.- Bayesian Support Vector Machines for Feature Ranking and Selection.- Nonlinear Feature Selection with the Potential Support Vector Machine.- Combining a Filter Method with SVMs.- Feature Selection via Sensitivity Analysis with Direct Kernel PLS.- Information Gain, Correlation and Support Vector Machines.- Mining for Complex Models Comprising Feature Selection and Classification.- Combining Information-Based Supervised and Unsupervised Feature Selection.- An Enhanced Selective Naïve Bayes Method with Optimal Discretization.- An Input Variable Importance Definition based on Empirical Data Probability Distribution.- New Perspectives in Feature Extraction.- Spectral Dimensionality Reduction.- Constructing Orthogonal Latent Features for Arbitrary Loss.- Large Margin Principles for Feature Selection.- Feature Extraction for Classification of Proteomic Mass Spectra: A Comparative Study.- Sequence Motifs: Highly Predictive Features of Protein Function.