Feature extraction : foundations and applications
This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. "This book compiles some very promising techniques, coming from an extremely smart collection of researchers, delivering their best ideas in...
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| Hlavní autor: | |
|---|---|
| Další autoři: | , , |
| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Vydání: | 1st ed. 2006. |
| Edice: | Studies in Fuzziness and Soft Computing
207 |
| On-line přístup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Poznámka: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Feature Extraction, Texte imprimé, 9783540862116 • Feature extraction, foundations and applications, Isabelle Guyon, Steve Gunn, Masoud Nikravesh... [et al.] (eds.), 2006, Berlin, Springer, 1 vol. (XXIV-778 p.), Studies in fuzziness and soft computing, 3-540-35487-5 • Feature Extraction, Texte imprimé, 9783662517710 |
Obsah:
- 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.