Entropy-based generation of supervised neural networks for classification of structured patterns.
Sperduti and Starita proposed a new type of neural network which consists of generalized recursive neurons for classification of structures. In this paper, we propose an entropy-based approach for constructing such neural networks for classification of acyclic structured patterns. Given a classification problem, the architecture, i.e., the number of hidden layers and the number of neurons in each hidden layer, and all the values of the link weights associated with the corresponding neural network are automatically determined. Experimental results have shown that the networks constructed by our method can have a better performance, with respect to network size, learning speed, or recognition accuracy, than the networks obtained by other methods.
Tsai
Hsien-Leing
HL
Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan.
Lee
Shie-Jue
SJ
D019277:Entropy; D016571:Neural Networks, Computer