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Multi-label dimensionality reduction /
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 minin...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, Florida :
CRC Press,
[2014]
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| Colección: | Chapman & Hall/CRC machine learning & pattern recognition series.
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| Temas: | |
| Acceso en línea: | Texto completo |
| Sumario: | 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. |
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| Descripción Física: | 1 online resource (206 pages) : illustrations |
| Bibliografía: | Includes bibliographical references. |
| ISBN: | 9781439806166 1439806160 9781439806159 1439806152 |