ABSTRACT
Among numerous pattern recognition methods the neural network approach has been the subject of much research due to its ability to learn from a given collection of representative examples. This paper concerns with the optimization of a weightless neural network, which decomposes a given pattern into several sets of n points, termed n-tuples. A population-based stochastic optimization technique, known as Particle Swarm Optimization (PSO), has been used to select an optimal set of connectivity patterns to improve the recognition performance of such .n-tuple. classifiers. The original PSO was refined by combining it with a bio-inspired technique called the Self-Organized Criticality (SOC) to add diversity in the population for finding better solutions. The hybrid algorithms were adapted for the n-tuple system and the performance was measured in selecting better connectivity patterns. The aim was to improve the discriminating power of the classifier in recognizing handwritten characters by exploiting the criticality dispersion in the swarm population. This paper presents the implementation of the hybrid model in greater detail with the effect of criticality dispersion in finding better solutions.
- Aleksander, I., and Stonham, T.J. (1979). Guide to Pattern Recognition using Random-access Memories, Computers and Digital Techniques, vol. 2, pp. 29--40.Google ScholarCross Ref
- Azhar, M. A. H. B. and Dimond, K.R. (2004). A Stochastic Search Algorithm to Optimize an N-tuple Classifier by Selecting Its Inputs, International Conference on Image Analysis and Recognition, Porto, Portugal, Springer-Verlag, September 29 -- October 1.Google ScholarCross Ref
- Azhar, M.A.H.B., Deravi, F. and Dimond, K.R.(2005). Relative Performances of Swarm Intelligence and Genetic Algorithm to Select Better N-tuples, Proceedings of the IEEE SMC UK-RI Chapter Conference on Applied Cybernetics, pp. 111--116, London, UK, 7--8 September.Google Scholar
- Bak, P. (1996). How nature works: the science of self-organized criticality (Copernicus, New York).Google Scholar
- Bishop, J.M., Crowe, A.A., Minchinton, P.R. and Mitchell, R.J. (1990). Evolutionary Learning to Optimise Mapping in n-Tuple Networks, IEE Colloquium on "Machine Learning", 28 June, Digest 1990/117.Google Scholar
- Bledsoe, W. and Browning, I. (1959). Pattern recognition and reading by machine, Proceedings of Eastern Joint Computer Conference, pp. 225--232, Birmingham.Google ScholarDigital Library
- Boettcher, S. and Paczuski, M. (1997). Aging in a Model of Self-Organized Criticality, Physical Review Letters, vol.79, Issue 5, pp. 889--892, The American Physical Society.Google ScholarCross Ref
- Deacon, J. (2006). The Really Easy Statistics Site, Biology Teaching Organisation, University of Edinburgh, http://www.biology.ed.ac.uk/research/groups/jdeacon/statistics/tress1.htmlGoogle Scholar
- Esmin, A. A. A., Aoki, A. R., and Lambert-Torres, G. (2002). Particle swarm optimization for fuzzy membership functions optimization. Proc. of the IEEE Int. Conf. on Systems, Man and Cybernetics, pp. 108--113.Google ScholarCross Ref
- Esquivel, S.C. and Coello Coello, C.A.(2003). On the Use of Particle Swarm Optimization with Multimodal Functions. In Proceedings of the IEEE Transactions on Evolutionary Computation, vol. 2, pp. 1130--1136.Google Scholar
- Fairhurst, M.C. and Stonham, T.J. (1976). A Class. System for Alpha-Numeric Characters Based on Learning Network Techniques, Digital Processes, vol. 2, pp. 321--339.Google Scholar
- Garcia, L.A.C. and Souto, M. C. P. (2004). Global Optimisation Methods for Choosing the Connectivity Pattern of N-tuple Classifiers, Proc. of the IEEE International Joint Conference on Neural Networks, Budapeste, pp. 2263--2266.Google Scholar
- Jain, A.K., Duin, R.P.W. and Mao, J. (2000). Statistical pattern recognition: A review, IEEE Trans. Pattern Anal. Machine Intell., vol. 22, pp. 4--38 Google ScholarDigital Library
- Jørgensen, T. M., Christensen, S. S. and Liisberg, C. (1995). Crossvalidation and information measures for RAM based neural networks, Proc. of the Weightless Neural Networks Workshop, University of Kent at Canterbury, UK, pp. 87--92.Google Scholar
- Jørgensen, T.M., Linneberg, C. (1999). Theoretical analysis and improved decision criteria for the n--tuple classifier, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, pp.336--347. Google ScholarDigital Library
- Kalyan, V., Thanmaya, P., Chilukuri, K. M. and Lisa, A. O. (2003). Optimization Using Particle Swarms with Near Neighbor Interactions, GECCO, July 11--16, Chicago, Illnois.Google Scholar
- Kennedy, J., and Eberhart, R. C. (1995). Particle swarm optimisation, Proc. of the 1995 IEEE Int. Conf. on Neural Networks (Perth, Australia).Google Scholar
- Krink, T. and Thomsen, R.(2001). Self--Organized Criticality and Mass Extinction in Evolutionary Algorithms, Proceedings of the Third Congress on Evolutionary Computation (CEC-2001), vol. 2, pp. 1155--1161.Google Scholar
- Løvbjerg, M. and Krink, T. (2002). Extending particle swarm optimisers with self-organized criticality, Proc. of the IEEE Congress on Evolutionary Computation, Honolulu, Hawaii USA. Google ScholarDigital Library
- Picton, P. (2000). Neural Networks (Grassroots Series), 2nd Edition, Palgrave Publishers Ltd. Google ScholarDigital Library
- Rickers, P., Thomsen, R. and Krink, T. (2000). Applying Self-Organized Criticality to the Diffusion Model, Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference, vol. 1, pp. 325--330, Morgan Kaufmann Publishers.Google Scholar
- Riget, J. and Vesterstrøm, J.S. (2002). A Diversity-Guided Particle Swarm Optimizer--The ARPSO, Technical report, Department of Computer Science, University of Aarhus.Google Scholar
- Rohwer, R. and Morciniec, M. (1998). The Theoretical and Experimental Status of the n-tuple Classifier, Neural Networks, 11(1): pp. 1--14. Google ScholarDigital Library
- Rohwer, R. and Cressy, D. (1989). Phoneme classification by boolean networks, Proceedings of the European Conference on Speech Communication and Technology, pp. 557--560.Google Scholar
- Settles, M. and Rylander, B. (2002).Neural network learning using particle swarm optimisers, Advances in Information Science and Soft Computing, pp. 224--226. WSEAS Press.Google Scholar
- Shi, Y, and Eberhart, R. (1998). Parameter selection in particle swarm optimization, in Evolutionary Programming VII, pp. 591--600, Springer, Lecture Notes in Computer Science 1447. Google ScholarDigital Library
- Tukey, J.W. (1977), Exploratory Data Analysis, Reading, MA: Addison-Wesley.Google Scholar
- Wilkinson, R., Geist, J., Janet, S., Grother, P., Burges, C., Creecy, R., Hammond, B., Hull, J., Larsen, N., Vogl, T., and Wilson, C. (1992). The first census optical character recognition systems conference, Technical Report NISTIR 4912, National Institute of Standards and Technology (NIST), Gaithersburg, USA.Google ScholarCross Ref
Index Terms
- Criticality dispersion in swarms to optimize n-tuples
Recommendations
CAPSO: Centripetal accelerated particle swarm optimization
Meta-heuristic search algorithms are developed to solve optimization problems. Such algorithms are appropriate for global searches because of their global exploration and local exploitation abilities. Swarm intelligence (SI) algorithms comprise a branch ...
Incremental Social Learning in Particle Swarms
Incremental social learning (ISL) was proposed as a way to improve the scalability of systems composed of multiple learning agents. In this paper, we show that ISL can be very useful to improve the performance of population-based optimization ...
A quick artificial bee colony (qABC) algorithm and its performance on optimization problems
Artificial bee colony (ABC) algorithm inspired by the foraging behaviour of the honey bees is one of the most popular swarm intelligence based optimization techniques. Quick artificial bee colony (qABC) is a new version of ABC algorithm which models the ...
Comments