Title :
Model parameter selection of support vector machines
Author :
Zhao, Mingyuan ; Tang, Ke ; Zhou, Mingtian ; Zhang, Fengli ; Zeng, Ling
Author_Institution :
Sch. of Comput. Sci. & Technol., Univ. of Electron. Sci. & Technol. of China, Chengdu
Abstract :
In order to optimize classification performance of support vector machines, analyzing character of model parameters on support vector machines with Gaussian kernel, using data of Ionosphere Database in UCI repository of machine learning database and electroencephalogram (EEG) experiment data to make and analyze the area search table, a new parametric distribution model is proposed. In order to search optimal points in model parameters of support vector machines, a new genetic algorithm based on parametric distribution model is proposed to improve classification performance of support vector machines remarkably.
Keywords :
genetic algorithms; learning (artificial intelligence); pattern classification; support vector machines; Gaussian kernel; classification performance; electroencephalogram experiment data; genetic algorithm; machine learning database; model parameter selection; parametric distribution model; support vector machines; Brain modeling; Databases; Electroencephalography; Genetic algorithms; Ionosphere; Kernel; Machine learning; Performance analysis; Support vector machine classification; Support vector machines; area search table; genetic algorithm; parameters selection; parametric distribution model; support vector machines;
Conference_Titel :
Cybernetics and Intelligent Systems, 2008 IEEE Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-1673-8
Electronic_ISBN :
978-1-4244-1674-5
DOI :
10.1109/ICCIS.2008.4670757
