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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) |
MARC
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| 100 | 1 | |a Sun, Liang, |e author |u Arizona State University, Tempe, USA | |
| 245 | 1 | 0 | |a Multi-Label Dimensionality Reduction / |c Liang Sun, Shuiwang Ji, Jieping Ye. |
| 250 | |a 1st. | ||
| 264 | 1 | |b Chapman and Hall/CRC, |c 2016. | |
| 300 | |a 1 online resource (208 pages : |b 14 illustrations) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
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| 520 | |a 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. | ||
| 542 | |f Copyright © Chapman and Hall/CRC 2014 |g 2014 | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 700 | 1 | |a Ji, Shuiwang, |d 1977- |e author. | |
| 700 | 1 | |a Ye, Jieping, |e author. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781439806166/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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