Applications of Fuzzy Support Vector Machines in Medical Engineering and Bioinformatics

• Main Authors: Li-Cheng Jin, 金立誠
• Other Authors: Cheng-Hong Yang
• Format: Others
• Language: en_US
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/49449955668694550309

碩士 === 國立高雄應用科技大學 === 電子與資訊工程研究所碩士班 === 92 === The support vector machine (SVM) is a new learning method and has shown comparable or better results than the neural networks on some applications. In this thesis, we applied SVM to two issues contained medical engineering and bioinformatics, respectively. In which, Morse code recognition and classification of multiple cancer types by gene expression were studied. At the same time, we exploit some strategies of SVM method included fuzzy logic and statistics theories, called fuzzy support vector machines. By using the strategies, we demonstrate that FSVM can achieve comparable efficiency as other approaches to deal with problems of medical engineering and bioinformatics. Therefore, the results of this study suggested that in the future, it will be a promising direction to apply FSVM on more applications for other research fields.