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Record Nr. |
UNISA996465879603316 |
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Titolo |
Subspace, Latent Structure and Feature Selection [[electronic resource] ] : Statistical and Optimization Perspectives Workshop, SLSFS 2005 Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers / / edited by Craig Saunders, Marko Grobelnik, Steve Gunn, John Shawe-Taylor |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 |
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ISBN |
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Edizione |
[1st ed. 2006.] |
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Descrizione fisica |
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1 online resource (X, 209 p.) |
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Collana |
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Theoretical Computer Science and General Issues, , 2512-2029 ; ; 3940 |
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Disciplina |
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Soggetti |
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Algorithms |
Computer science—Mathematics |
Mathematical statistics |
Computer science |
Artificial intelligence |
Computer vision |
Pattern recognition systems |
Probability and Statistics in Computer Science |
Theory of Computation |
Artificial Intelligence |
Computer Vision |
Automated Pattern Recognition |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references and author index. |
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Nota di contenuto |
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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- |
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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. |
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