Numerosity and the consolidation of episodic memory
Authors: 
J. G. Wallace, K. BluffAuthors Info & Claims
Abstract
Different techniques for extracting Artificial Neural Networks (ANN) rules have been used up to the present time, but most of them have focused on certain types of networks and their training. However, there are practically no methods which deal with ANN rule-discovery as systems that are independent from their architecture, training, and internal distribution of weights, connections, and activation functions. This paper proposes a method based on Genetic Programming (GP) with the purpose of achieving the generalization capacity characteristic of ANNs, by means of symbolic rules which can be understood by human beings.
References
[1]
Tickle, A.B., Andrews, R Golea, M. Diederich, J. "The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks". IEEE Transaction on Neural Networks, vol. 9, no. 6, pag. 1057-1068, 1998.
[2]
Cramer, N.L. "A Representation for the Adaptive Generation of Simple Sequential Programs", Grefenstette: Proceedings of First International Conference on Genetic Algorithms, 1985.
[3]
Fujiki C. "Using the Genetic Algorithm to Generate Lisp Source Code to Solve the Prisoner's Dilemma", International Conf on GAs, pp. 236-240, 1987.
[4]
Friedberg R.M. "A learning machine: Part P, IBM Journal of Research and Development, 2(1) pag. 2-13, 1958.
[5]
Koza J. "Genetic Programming. On the Programming of Computers by means of Natural Selection". The Mil Press, Cambridge, Massachusetts, 1992.
[6]
Jang J., Sun C. "Functional equivalence between radial basis function networks and fuzzy inference systems". IEEE Transactions on Neural Networks, vol. 4, paginas 156-158, 1992.
[7]
Buckley J.J., Hayashi Y., Czogala E. "On the equivalence of neural nets and fuzzy expert systems", Fuzzy Sets Systems, no. 53, pag. 129-134, 1993.
[8]
Benitez J. M., Castro J. L., Requena I. "Are artificial neural networks black boxes?". IEEE Transactions on Neural Networks, vol. 8, no. 5, pag. 1156-1164, 1997.
[9]
Andrews R. Diederich J. & Tickle A. "A Survey and Critique of Techniques For Extracting Rules From Trained Artificial Neural Networks". Knowledge Based Systems vol. 8, pag. 373-389, 1995.
[10]
Towell G., Shavlik J. W. "Knowledge-Based Artificial Neural Networks". Artificial Intelligence, 70, pag. 119-165.
[11]
Thrun S. "Extracting rules from networks with distributed representations". Advances in Neural Information Processing Systems (NIPS) 7, G. Tesauro, D. Touretzky, T. Leen (eds), MIT Press.
[12]
Pop E., Hayward R., Diederich J. "RULENEG: Extracting Rules from a Trained ANN by Stepwise Negation". Queensland University of Technology, Neurocomputing Research Centre. QUT NRC Technical report, 1994.
[13]
Tickle A. B., Orlowski M., Diedrich J. "DEDEC: A methodology for extracting rules from trained artificial neural networks". Queensland University of Technology, Neurocomputing Research Centre. QUT NRC Technical report, 1996.
[14]
Chalup S., Hayward R., Diedrich J. "Rule extraction from artificial neural networks trained on elementary number classification task". Queensland University of Technology, Neurocomputing Research Centre. QUT NRC Technical report, 1998.
[15]
Visser U., Tickle A., Hayward R., Andrews R. "Rule-Extraction from trained neural networks: Different techniques for the determination of herbicides for the plant protection advisory system PRO_PLANT". Proceedings of the rule extraction from trained artificial neural networks workshop, Brighton, UK, pag. 133-139.1996.
[16]
Keedwell E., Narayanan A., Savic D. "Creating rules from trained neural networks using genetic algorithms". In the International Journal of Computers, Systems and Signals (IJCSS), vol. 1, no. 1, pag. 30-42, 2000.
[17]
Wong M.L., Leung K.S. "Data Mining using Grammar Based Genetic Programming and Applications", Kluwer Academic Publishers, 2000.
[18]
Engelbrecht A.P., Rouwhorst S.E., Schoeman L. "A Building Block Approach to Genetic Programming for Rule Discovery", Data Mining: A Heuristic Approach, Abbass, R. Sarkar, C. Newton editors, Idea Group Publishing, 2001.
[19]
Fisher R.A. "The use of multiple measurements in taxonomic problems", Annals of Eugenics, vol. 7, pag. 179-188, 1936.
[20]
Rivero D., Rabunal J., Dorado J., Pazos A., Pedreira N. "Extracting knowledge from databases with Genetic Programming: Iris flower classification problem", Proceedings of CIMCA and IAWTIC, 2003.
[21]
Duch W., Adamczak R., Grabczewski K. "A new methodology of extraction, optimisation and application of crisp and fuzzy logical rules", IEEE Transactions on Neural Networks, vol. 11, no. 2, 2000.
[22]
Martínez A., Goddard J. "Definitión de una red neuronal para clasiiicación por medio de un programa evolutivo", revista mexicana de ingeniería biomédica, vol. 22, 1, pp. 4-11, 2001.
Recommendations
On numerosity of deep neural networks
NIPS '20: Proceedings of the 34th International Conference on Neural Information Processing SystemsRecently, a provocative claim was published that number sense spontaneously emerges in a deep neural network trained merely for visual object recognition. This has, if true, far reaching significance to the fields of machine learning and cognitive ...
Neural network simulation on shared-memory vector multiprocessors
Supercomputing '89: Proceedings of the 1989 ACM/IEEE conference on SupercomputingWe simulate three neural networks on a vector multiprocrssor. The training time can be reduced significantly especially when the training data size is large. These three neural networks are: 1) the feedforward network, 2) the recurrent network and 3) ...
Memory Networks
MM '22: Proceedings of the 30th ACM International Conference on MultimediaMemory Networks are models equipped with a storage component where information can generally be written and successively retrieved for any purpose. Simple forms of memory networks like the popular recurrent neural networks (RNN), LSTMs or GRUs, have ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 03 June 2003
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 14 Dec 2024