|
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNISA996465879603316 |
|
|
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 |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Edizione |
[1st ed. 2006.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (X, 209 p.) |
|
|
|
|
|
|
Collana |
|
Theoretical Computer Science and General Issues, , 2512-2029 ; ; 3940 |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
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 |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
Bibliographic Level Mode of Issuance: Monograph |
|
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references and author index. |
|
|
|
|
|
|
Nota di contenuto |
|
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. |
|
|
|
|
|
| |