Title: Adaptive Prototype Learning Algorithms
We proposed a number of adaptive prototype learning (APL) algorithms. They all employ the same algorithmic scheme to determine the number and location of prototypes, but differ in the use of samples or the weighted averages of samples as prototypes, and also in the assumption of distance measures. To understand these algorithms from a theoretical viewpoint, we addressed their convergence properties, as well as their asymptotic Bayes-risk efficiency under certain conditions. Applying the proposed algorithms to twelve UCI benchmark datasets, we demonstrated that they outperform many instance-based learning algorithms, the k-nearest neighbor rule, and support vector machines in terms of average test accuracy. Related publication:
F. Chang, C.-C. Lin, and C.-J. Lu, Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies, Journal of Machine Learning Research, vol. 7, pp. 2125-2148, 2006.
標題：調適性原型學習算則
我們提出數種調適性原型學習（APL）算則。它們使用同樣的算則模式來決定原型的數量與位置，彼此的差異在於使用樣本或者樣本的加權平均作為原型，以及對於距離的假定。為了從理論觀點來瞭解這些算則，我們討論它們收斂的性質以及在特定條件下成立的漸近貝氏風險效性。應用這些算則在UCI基準資料上，我們驗證了它們在平均測試的正確率上優於很多以個例為基礎的學習算則，k-最近鄰居法則，以及支持向量機。相關的論文請見：
F. Chang, C.-C. Lin, and C.-J. Lu, Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies, Journal of Machine Learning Research, vol. 7, pp. 2125-2148, 2006.