Output Effect Evaluation Based on Input Features in Neural Incremental Attribute Learning for Better Classification Performance
學年 103
學期 1
出版(發表)日期 2014-12-29
作品名稱 Output Effect Evaluation Based on Input Features in Neural Incremental Attribute Learning for Better Classification Performance
作品名稱(其他語言)
著者 Ting Wang; Guan, Sheng-Uei; Ka Lok Man; Jong Hyuk Park; Hsu, Hui-Huang
單位 淡江大學資訊工程學系
出版者 Basel: M D P I AG
著錄名稱、卷期、頁數 Symmetry 7(1), pp.53-66
摘要 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 very clear. In this study, the feasibility of IAL is verified by mathematical approaches. Moreover, experimental results derived by IAL neural networks on benchmarks also confirm the mathematical validation.
關鍵字 pattern classification;neural networks;incremental attribute learning;feature ordering;discrimination ability
語言 en
ISSN 2073-8994
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度
國別 CHE
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/103197 )

機構典藏連結