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...
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: | |
Online Access: | http://www.mdpi.com/2073-8994/7/1/53 |
Similar Items
-
Neural Incremental Attribute Learning in Groups
by: Fangzhou Liu, et al.
Published: (2015-06-01) -
Incremental Attribute Reduction Method Based on Chi-Square Statistics and Information Entropy
by: Na Su, et al.
Published: (2020-01-01) -
Attribute Learning for SAR Image Classification
by: Chu He, et al.
Published: (2017-04-01) -
Incremental Learning in Terms of Output Attributes
by: Guan, Sheng-Uei, et al.
Published: (2004-06-01) -
Boosting performance of incremental IDR/QR LDA - from sequential to chunk
by: Peng, Yiming
Published: (2011)