関数空間上におけるノルムを用いた干渉量の定義と追加学習

抄録

This paper proposes new incremental learning methods of generalized radial basis function (GRBF). We define a magnitude of interference as the norm of the distance between the output functions before learning and the one after learning. During the learning phase, the GRBF modifies its parameters so as to minimize an objective function described by the sum of an error function and the magnitude of interference.