Multi-Label Dimensionality Reduction e-bog
436,85 DKK
(inkl. moms 546,06 DKK)
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
E-bog
436,85 DKK
Forlag
Chapman and Hall/CRC
Udgivet
19 april 2016
Længde
208 sider
Genrer
KCHS
Sprog
English
Format
pdf
Beskyttelse
LCP
ISBN
9781439806166
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