研究論文
疎な関数表現を与える学習機械を用いたサンプルからの規則抽出
2003 年 10 巻 3 号 p. 139-146
詳細
2003 年 10 巻 3 号 p. 139-146
We propose a novel rule extraction algorithm adopting the mathematical model called Basis Pursuit (Chen, et al. 1998), where it is represented by a linear combination of kernel functions (similar to MLP or Support Vector Machines) but gives sparse function representation compared to those models. In this algorithm, a number of logical rules are set to the kernel functions in advance. Applying a linear programming method, we obtain classification rules as a small subset of them. If less logical rules are discovered, they will be the core knowledge in a database. We applied our algorithm to several known benchmark problems and its effectualness is verified.
