Efficient Learning in Adaptive Processing of Data Structures

Authors:
Siu-Yeung Cho;Zheru Chi;Zhiyong Wang;Wan-Chi Siu
Affiliations:
Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.;Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. e-mail: enzheru@polyu.edu ...;Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.;Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
Venue:
Neural Processing Letters
Year:
2003

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Abstract

Many researchers have explored the use of neural network models for the adaptive processing of data structures. The learning formulation for one of the models is known as the Backpropagation Through Structure (BPTS) algorithm. The main limitations of the BPTS algorithm are attributed to the problems of slow convergence speed and long-term dependency. In this Letter, a novel heuristic algorithm is proposed. The idea of this algorithm is to optimize the free parameters of the node representation in data structure by using a hybrid type of learning algorithm. Encouraging results achieved demonstrate that this proposed algorithm outperforms the BPTS algorithm.