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[null,"1.","Record Nr.",null,"UNISA996465879603316"]
[null,null,"Titolo",null,"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"]
[null,null,"Pubbl/distr/stampa",null,"Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006"]
[null,null,"ISBN",null,"3-540-34138-2"]
[null,null,"Edizione",null,"[1st ed. 2006.]"]
[null,null,"Descrizione fisica",null,"1 online resource (X, 209 p.)"]
[null,null,"Collana",null,"Theoretical Computer Science and General Issues, , 2512-2029 ; ; 3940"]
[null,null,"Disciplina",null,"003/.1"]
[null,null,"Soggetti",null,"Algorithms"]
[{"$nulls":4},"Computer science—Mathematics"]
[{"$nulls":4},"Mathematical statistics"]
[{"$nulls":4},"Computer science"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Pattern recognition systems"]
[{"$nulls":4},"Probability and Statistics in Computer Science"]
[{"$nulls":4},"Theory of Computation"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer Vision"]
[{"$nulls":4},"Automated Pattern Recognition"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and author index."]
[null,null,"Nota di contenuto",null,"Invited Contributions -- Discrete Component Analysis -- Overview and Recent Advances in Partial Least Squares -- Random Projection, Margins, Kernels, and Feature-Selection -- Some Aspects of Latent Structure Analysis -- Feature Selection for Dimensionality Reduction -- Contributed Papers -- Auxiliary Variational Information Maximization for Dimensionality Reduction -- Constructing Visual Models with a Latent Space Approach -- Is Feature Selection Still Necessary? -- Class-Specific Subspace Discriminant Analysis for High-Dimensional Data -- Incorporating Constraints and Prior Knowledge into Factorization Algorithms – An Application to 3D Recovery -- A Simple Feature "]
[{"$nulls":4},"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."]