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1. |
Record Nr. |
UNISA990003433760203316 |
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Titolo |
Grand Larousse de la langue française en sept volumes / [sous la direction de Louis Guilbert ... [et al.] |
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Pubbl/distr/stampa |
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Descrizione fisica |
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Disciplina |
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Soggetti |
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Lingua francese -- Enciclopedie e dizionari |
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Collocazione |
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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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2. |
Record Nr. |
UNINA9910453732903321 |
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Autore |
Sun Liang |
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Titolo |
Multi-label dimensionality reduction / / Liang Sun, Shuiwang Ji, and Jieping Ye |
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Pubbl/distr/stampa |
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Boca Raton, FL : , : CRC Press, , [2014] |
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©2014 |
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ISBN |
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0-429-14820-8 |
1-4398-0616-0 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (206 p.) |
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Collana |
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Chapman & Hall/CRC machine learning & pattern recognition series |
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Disciplina |
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Soggetti |
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Computational complexity |
Machine learning |
Pattern perception |
Electronic books. |
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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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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Cover; Series; Contents; Preface; Symbol Description; Chapter 1: Introduction; Chapter 2: Partial Least Squares; Chapter 3: Canonical Correlation Analysis; Chapter 4: Hypergraph Spectral Learning; Chapter 5: A Scalable Two-Stage Approach for Dimensionality Reduction; Chapter 6: A Shared-Subspace Learning Framework; Chapter 7: Joint Dimensionality Reduction and Classification; Chapter 8: Nonlinear Dimensionality Reduction: Algorithms and Applications; Appendix Proofs; References; Back Cover |
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Sommario/riassunto |
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Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks a unified treatment of multi-label dimensionality reduction that incorporates both algorithmic developments and applications. Addressing this shortfall, Multi-Label Dimensionality Reduction covers the methodological developments, theoretical properti |
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