臺灣大學: 資訊網路與多媒體研究所林智仁鐘博瀚Chung, Po-HanPo-HanChung2013-03-222018-07-052013-03-222018-07-052011http://ntur.lib.ntu.edu.tw//handle/246246/251231Recently, many people handle natural language processing (NLP) tasks via support vector machines (SVM) with polynomial kernels. However, kernel computation is time consuming. Chang et al. (2010) have proposed mapping data by low-degree polynomial functions and applying fast linear-SVM methods. For data with many features, they have considered condensing data to effectively solve some memory and computational difficulties. In this thesis, we investigate Chang et al.''s methods and give implementation details. We conduct experiments on four NLP tasks to show the viability of our implementation.2284755 bytesapplication/pdfen-US自然語言處理支持向量機多項式映射Natural language processingSupport vector machinePolynomial mappingthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/251231/1/ntu-100-R98944031-1.pdf