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...
| Otros Autores: | , , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press
[2014]
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| Edición: | 1st edition |
| Colección: | Chapman & Hall/CRC machine learning & pattern recognition series
Chapman & Hall/CRC machine learning & pattern recognition series. |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628080106719 |
| 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 developments, theoretical properti |
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| Notas: | Description based upon print version of record. |
| Descripción Física: | 1 online resource (206 p.) |
| Bibliografía: | Includes bibliographical references. |
| ISBN: | 9780429148200 9781439806166 |
