Title :
Selection of tuning parameters for support vector machines
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI, USA
Abstract :
Support vector machines have become important in classification, biometrics, machine learning and pattern recognition. However, successful application requires selection of various tuning parameters such as kernel parameters and penalty or margin parameters. We apply a new technique for this problem which provides very simple structure for the automatic selector.
Keywords :
biometrics (access control); learning (artificial intelligence); pattern classification; support vector machines; tuning; automatic selector structure; biometrics; classification; kernel parameters; machine learning; margin parameters; pattern recognition; penalty parameters; support vector machines; tuning parameter selection; Application software; Biometrics; Inverse problems; Kernel; Machine learning; Pattern recognition; Supervised learning; Support vector machine classification; Support vector machines; Training data;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on
Print_ISBN :
0-7803-8874-7
DOI :
10.1109/ICASSP.2005.1416284
