DocumentCode
1567303
Title
Compensating Hypothesis by Negative Data
Author
Jiang, Fuhua ; Preethy, A.P. ; Zhang, Yan-Qing
Author_Institution
Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA
Volume
3
fYear
2005
Firstpage
1986
Lastpage
1990
Abstract
The properties of training data set such as size, distribution and number of attributes significantly contribute to the generalization error of a learning machine. A data set not well-distributed is prone to lead to a model with partial overfitting. The approach proposed in this paper for the binary classification enhances the useful data information by mining negative data based on the understanding of Chinese traditional Yin-Yang theory
Keywords
data mining; learning (artificial intelligence); Chinese traditional Yin-Yang theory; binary classification; generalization error; learning machine; negative data mining; partial overfitting; Boosting; Computer errors; Computer science; Data mining; Machine learning; Machine learning algorithms; Support vector machines; Testing; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location
Beijing
Print_ISBN
0-7803-9422-4
Type
conf
DOI
10.1109/ICNNB.2005.1615013
Filename
1615013
Link To Document