Output Effect Evaluation Based on Input Features in Neural Incremental Attribute Learning for Better Classification Performance

Output Effect Evaluation Based on Input Features in Neural Incremental Attribute Learning for Better Classification Performance

Machine learning is a very important approach to pattern classification. This paper provides a better insight into Incremental Attribute Learning (IAL) with further analysis as to why it can exhibit better performance than conventional batch training. IAL is a novel supervised machine learning strat...

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Bibliographic Details
Main Authors: Ting Wang, Sheng-Uei Guan, Ka Lok Man, Jong Hyuk Park, Hui-Huang Hsu
Format: Article
Language:English
Published: MDPI AG 2015-01-01
Series:Symmetry
Subjects:
pattern classification
neural networks
incremental attribute learning
feature ordering
discrimination ability
Online Access:http://www.mdpi.com/2073-8994/7/1/53

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http://www.mdpi.com/2073-8994/7/1/53

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