Abstract
Data Mining is an analytic process designed to explore data (usually large amounts of data - typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. One of the most commonly used techniques in data mining, Artificial Neural Networks provide non-linear predictive models that learn through training and resemble biological neural networks in structure. This paper deals with a new adaptive neural network model: a feed-forward higher order neural network with a new activation function called neuron-adaptive activation function. Experiments with function approximation and stock market movement analysis have been conducted to justify the new adaptive neural network model. Experimental results have revealed that the new adaptive neural network model presents several advantages over traditional neuron-fixed feed-forward networks such as much reduced network size, faster learning, and more promising financial analysis.
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References
Adriaans, P., Zantinge, D.: Data Mining. Addison-Wesley, Reading (1996)
Sarker, R.A., Abbass, H.A., Newton, C.S.: Data mining: A Heuristic Approach. Idea Group Pub., Hershey (2002)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)
Zhang, M., Xu, S., Fulcher, J.: Neuron-adaptive Higher Order Neural Network Models for Automated Financial Data Modelling. IEEE Transactions on Neural Networks 13(1) (2002)
Vecci, L., Piazza, F., Uncini, A.: Learning and Approximation Capabilities of Adaptive Spline Activation Function Neural Networks. Neural Networks 11, 259–270 (1998)
Chen, C.T., Chang, W.D.: A Feedforward Neural Network with Function Shape Autotuning. Neural Networks 9(4), 627–641 (1996)
Campolucci, P., Capparelli, F., Guarnieri, S., Piazza, F., Uncini, A.: Neural Networks with Adaptive Spline Activation Function. In: Proceedings of IEEE MELECON 96, Bari, Italy, pp. 1442–1445 (1996)
Hu, Z., Shao, H.: The Study of Neural Network Adaptive Control Systems. Control and Decision 7, 361–366 (1992)
Yamada, T., Yabuta, T.: Remarks on a Neural Network Controller Which Uses an Auto-tuning Method for Nonlinear Functions. In: IJCANN, vol. 2, pp. 775–780 (1992)
Lee, Y.C., Doolen, G., Chen, H., Sun, G., Maxwell, T., Lee, H., Giles, C.L.: Machine Learning Using a Higher Order Correlation Network. Physica D: Nonlinear Phenomena 22, 276–306 (1986)
Lippman, R.P.: Pattern Classification Using Neural Networks. IEEE Commun. Mag. 27, 47–64 (1989)
Psaltis, D., Park, C.H., Hong, J.: Higher Order Associative Memories and Their Optical Implementations. Neural Networks 1, 149–163 (1988)
Reid, M.B., Spirkovska, L., Ochoa, E.: Simultaneous Position, Scale, Rotation Invariant Pattern Classification Using Third-order Neural Networks. Int. J. Neural Networks 1, 154–159 (1989)
Wood, J., Shawe-Taylor, J.: A Unifying Framework for Invariant Pattern Recognition. Pattern Recognition Letters 17, 1415–1422 (1996)
Giles, C.L., Maxwell, T.: Learning, Invariance, and Generalization in Higher Order Neural Networks. Applied Optics 26(23), 4972–4978 (1987)
Redding, N.J., Kowalczyk, A., Downs, T.: Constructive Higher-order Network Algorithm That Is Polynomial Time. Neural Networks 6, 997–1010 (1993)
Kosmatopoulos, E.B., Polycarpou, M.M., Christodoulou, M.A., Ioannou, P.A.: High-order Neural Network Structures for Identification of Dynamical Systems. IEEE Transactions on Neural Networks 6(2), 422–431 (1995)
Thimm, G., Fiesler, E.: High-order and Multilayer Perceptron Initialization. IEEE Transactions on Neural Networks 8(2), 349–359 (1997)
Rumelhart, D.E., McClelland, J.L.: Parallel Distributed Computing: Exploration in the Microstructure of Cognition. MIT Press, Cambridge (1986)
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Xu, S., Zhang, M. (2007). A New Adaptive Neural Network Model for Financial Data Mining. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_147
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DOI: https://doi.org/10.1007/978-3-540-72383-7_147
Publisher Name: Springer, Berlin, Heidelberg
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