Title :
Subspace classifier in reproducing kernel Hilbert space
Author_Institution :
Electrotech. Lab., Ibaraki, Japan
Abstract :
To improve the performance of subspace classifier, it is effective to reduce the dimensionality of the intersections between subspaces. For this purpose, the feature space is mapped implicitly to a high dimensional reproducing kernel Hilbert space and the subspace classifier is applied in this space. As a result of Hiragana recognition experiment, our classifier outperformed the conventional subspace classifier
Keywords :
feature extraction; handwritten character recognition; learning (artificial intelligence); neural nets; pattern classification; Hiragana recognition; dimensionality; feature extraction; handwritten character recognition; kernel Hilbert space; learning; pattern classification; subspace classifier; Hilbert space; Kernel; Laboratories; Pattern recognition; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.836045
