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Symmetry • Vol 7 • No 1
Output Effect Evaluation Based on Input Features in Neural Incremental Attribute Learning for Better Classification Performance
January 2015 • Ting Wang, Sheng-Uei Guan, Ka Lok Man, Jong Hyuk Park, Hui-Huang Hsu
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 strategy, which gradually trains features in one or more chunks. Previous research showed that IAL can obtain lower classification error rates than a conventional batch training approach. Yet the reason for that is still not…
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