In this paper a new Higher Order Neural Network (HONN) model is introduced and applied in several data mining tasks. Data Mining extracts hidden patterns and valuable information from large databases. A hyperbolic tangent function is used as the neuron activation function for the new HONN model. Experiments are conducted to demonstrate the advantages and disadvantages of the new HONN model, when compared with several conventional Artificial Neural Network (ANN) models: Feedforward ANN with the sigmoid activation function; Feedforward ANN with the hyperbolic tangent activation function; and Radial Basis Function (RBF) ANN with the Gaussian activation function. The experimental results seem to suggest that the new HONN holds higher generalization capability as well as abilities in handling missing data.
History
Publication title
Proceedings of the Second International Symposium on Computational Mechanics and the 12th International Conference on the Enhancement and Promotion of Computational Methods in Engineering and Science
Editors
Jane Wei-Zhen Lu, Andrew YT Lerung, Vai Pan Iu, Kai Meng Mok
Pagination
1507-1511
ISBN
978-0-7354-0778-7
Department/School
School of Information and Communication Technology
Publisher
American Institute of Physics
Place of publication
United States of America
Event title
International Symposium on Computational Mechanics (ISCM) and Enhancement and Promotion of Computational Methods in Engineering and Science (EPMESC)