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| Artikel-Nr.: 5667A-9783540341376 Herst.-Nr.: 9783540341376 EAN/GTIN: 9783540341376 |
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| Invited Contributions.- Discrete Component Analysis.- Overview and Recent Advances in Partial Least Squares.- Random Projection, Margins, Kernels, and Feature-Selection.- Some Aspects of Latent Structure Analysis.- Feature Selection for Dimensionality Reduction.- Contributed Papers.- Auxiliary Variational Information Maximization for Dimensionality Reduction.- Constructing Visual Models with a Latent Space Approach.- Is Feature Selection Still Necessary?.- Class-Specific Subspace Discriminant Analysis for High-Dimensional Data.- Incorporating Constraints and Prior Knowledge into Factorization Algorithms - An Application to 3D Recovery.- A Simple Feature Extraction for High Dimensional Image Representations.- Identifying Feature Relevance Using a Random Forest.- Generalization Bounds for Subspace Selection and Hyperbolic PCA.- Less Biased Measurement of Feature Selection Benefits. Weitere Informationen: | | Author: | Craig Saunders; Marko Grobelnik; Steve Gunn; John Shawe-Taylor | Verlag: | Springer Berlin | Sprache: | eng |
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| Weitere Suchbegriffe: allgemeine Informatikbücher - englischsprachig, allgemeine informatikbücher - englischsprachig, Analyse / Datenanalyse, Datenanalyse, Informatik, 3D, Bayesian inference, STATISTICA, algorithm, algorithmic learning, algorithms, calculus, image reconstruction |
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