5英文原文,基于支持向量机的说话人识别系统径向基核函数算法研究及参数优化.doc

5英文原文,基于支持向量机的说话人识别系统径向基核函数算法研究及参数优化.docBiological applications of support vector machines
Abstract
One of the major tasks in bioinformatics is the classification and prediction of biological the rapid increase in size of the biological databanks, it is essential to use computer programs to automate the classification process. At present, the computer programs that give the best prediction performance are support vector machines (SVMs). This is because SVMs are designed to maximise the margin to separate two classes so that the trained model generalises well on unseen data. Most other computer programs implement a classifier through the minimisation of error occurred in training, which leads to poorer of this, SVMs have been widely applied to many areas of bioinformatics including protein function prediction, protease functional site recognition, transcription initiation site prediction and gene expression data classification. This paper will discuss the principles of SVMs and the applications of SVMs to the analysis of biological data, mainly protein and DNA sequences.
INTRODUCTION
With the rapid increase in size of the biological databanks, understanding the data has become critical. Such an understanding could lead us to the elucidation of the secrets of life or ways to prevent certain currently non-curable diseases such as HIV. Although laboratory experiment is the most effect