A New Hidden Error Function for Training of Multilayer Perceptrons

다층 퍼셉트론의 층별 학습 가속을 위한 중간층 오차 함수

  • Published : 2005.12.01


LBL(Layer-By-Layer) algorithms have been proposed to accelerate the training speed of MLPs(Multilayer Perceptrons). In this LBL algorithms, each layer needs a error function for optimization. Especially, error function for hidden layer has a great effect to achieve good performance. In this sense, this paper proposes a new hidden layer error function for improving the performance of LBL algorithm for MLPs. The hidden layer error function is derived from the mean squared error of output layer. Effectiveness of the proposed error function was demonstrated for a handwritten digit recognition and an isolated-word recognition tasks and very fast learning convergence was obtained.


Multilayer Perceptrons;Layer-By-Layer Training;Error Function