Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An Effective Machine Learning Algorithm using Momentum Scheduling
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
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Pattern recognition and classification problems are most popular issue in machine learning, and it seem that they meet their second golden age with bioinformatics. However, the dataset of bioinformatics has several distinctive characteristics compared to the data set in classical pattern recognition and classification research area. One of the most difficulties using this theory in bioinformatics is that raw data of DNA or protein sequences cannot be directly used as input data for machine learning because every sequence has different length of its own code sequences. Therefore, this paper introduces one of the methods to overcome this difficulty, and also argues that the capability of generalization in this method is very poor as showing simple experiments. Finally, this paper suggests different approach to select the fixed number of effective features by using Support Vector Machine, and noise whitening method. This paper also defines the criteria of this suggested method and shows that this method improves the precision of diagnosing abnormal protein sequences with experiment of classifying ovarian cancer data set.