Learning by Switching Knowledge Representations-Limiting the Number of Stored Data
Yūji Matsumoto, Motohide Umano, Masahiro Tomaru, Kazuhisa Seta · 2006
When we solve a problem, we initially have no knowledge and we memorize the raw data with observing data. Finally we have general knowledge for solving the problem. To simulate this learning process, we proposed a learning method with switching different levels of knowledge representations, reconstructing knowledge and switching reasoning methods. In the system, all given data are stored to generate new knowledge, but it is different from the one of our human's knowledge acquisition, in which we just memorize a limit number of data. Therefore, we limit it and when the number of stored data exceeds specified size, the system throws away the oldest data. In the simulation, we apply the method to the data set whose classes are changed periodically, and get a better result than the old method.