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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...
| Autores principales: | , , |
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| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Chapman and Hall/CRC,
2016.
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| Edición: | 1st. |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| 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. |
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| Descripción Física: | 1 online resource (208 pages : 14 illustrations) |
| ISBN: | 9781439806166 1439806160 9781439806159 1439806152 9780429148200 0429148208 |