林智仁臺灣大學：工業工程學研究所Liu, Tzu-JungTzu-JungLiu2007-11-262018-06-292007-11-262018-06-292004http://ntur.lib.ntu.edu.tw//handle/246246/51208Support vector machine (SVM) is a promising technique for data classification and regression. However, it provides only decision values but not posterior probability estimates. As many applications require probability outputs, it is essential to study how to transform SVM outputs to probability values. In this thesis, we study and compare various methods.Chapter 1. Introduction - 1 Chapter 2. Support Vector Machine - 3 Chapter 3. Probability Methods - 8 Chapter 4. Methods of Experiments - 21 Chapter 5. Results - 23 Chapter 6. Conclusions - 41243473 bytesapplication/pdfen-US機率輸出SVMthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/51208/1/ntu-93-R91546024-1.pdf