國立臺灣大學電機工程學系暨研究所王勝德2006-07-252018-07-062006-07-252018-07-062004-07-31http://ntur.lib.ntu.edu.tw//handle/246246/7971本計畫研究以多策略的學習為基礎的智慧型代理人，並應用在行動電子商務方面。我們研 究以支持向量機(SVM)的學習方法，智慧型代理人學習資料間的關連，以便建立初始的知 識庫和規則庫；支持向量機和其他資料分類的方法一樣，可以從所輸入的資料學習決策面， 此決策面可用來解決分類或回歸的問題。在很多應用中，例如含有雜訊的資料，資料點適 合加上權重來考量其重要性。在我們過去的研究，已提出以模糊從屬函數的觀念來表達權 重，並重新對SVM 命題，提出模糊支持向量機 (FSVM)，以便可以學習到更好的決策面。 模糊支持向量機提供了一個方法來解決資料混有較不重要的例外或雜訊問題，但對於模糊 從屬函數的決定卻並無一定的方法，本研究的目的之一即是要提出一個較系統化或自動化 的方法來決定從屬函數的參數。為了此目標，我們提出兩個因數，一為信心因數(confident factor)，另一為捨棄因數 (trashy factor)，並利用一個對應函數來找出模糊從屬函數的參數， 最後，我們以一些範例來，驗證所提方法的有效性。In this project we investigated the architectures and applications of multistrategy learning for distributed intelligent agents for mobile-commerce. It makes use of multistrategy learning and hybrid knowledge base such that the intelligent agents can adapt themselves to environmental changes via constructing the knowledge base and the rule base. Support vector machines like other classification approaches aim to learn the decision surface from the input points for classification problems or regression problems. In many applications, each input points may be associated with different weightings to reflect their relative strengths to conform to the decision surface. In our previous research, we applied a fuzzy membership to each input point and reformulate the support vector machines to be fuzzy support vector machines (FSVMs) such that different input points can make different contributions to the learning of the decision surface. FSVMs provide a method for the classification problem with noises or outliers. However, there is no general rule to determine the membership of each data point. We can manually associate each data point with a fuzzy membership that can reflect their relative degrees as meaningful data. To enable automatic setting of memberships, we introduce two factors in training data points, the confident factor and the trashy factor, and automatically generate fuzzy memberships of training data points from a heuristic strategy by using these two factors and a mapping function. We investigate and compare two strategies in the experiments and the results show that the generalization error of FSVMs are comparable to other methods on benchmark datasets.application/pdf380646 bytesapplication/pdfzh-TW國立臺灣大學電機工程學系暨研究所智慧型代理人支持向量機模糊支持向量機模糊從屬函數參數自動設定intelligent agentssupport vector machinesfuzzy membershipfuzzy SVMnoisy data traningreporthttp://ntur.lib.ntu.edu.tw/bitstream/246246/7971/1/922213E002009.pdf