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|Title:||A stochastic connectionist approach for global optimization with application to pattern clustering||Authors:||Babu, G.P.
|Issue Date:||Feb-2000||Citation:||Babu, G.P., Murty, M.N., Keerthi, S.S. (2000-02). A stochastic connectionist approach for global optimization with application to pattern clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 30 (1) : 10-24. ScholarBank@NUS Repository. https://doi.org/10.1109/3477.826943||Abstract:||In this paper, a stochastic connectionist approach is proposed for solving function optimization problems with real-valued parameters. With the assumption of increased processing capability of a node in the connectionist network, we show how a broader class of problems can be solved. As the proposed approach is a stochastic search technique, it avoids getting stuck in local optima. Robustness of the approach is demonstrated on several multi-modal functions with different numbers of variables. Optimization of a well-known partitional clustering criterion, the squared-error criterion (SEC), is formulated as a function optimization problem and is solved using the proposed approach. This approach is used to cluster selected data sets and the results obtained are compared with that of the K-means algorithm and a simulated annealing (SA) approach. The amenability of the connectionist approach to parallelization enables effective use of parallel hardware. © 2000 IEEE.||Source Title:||IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics||URI:||http://scholarbank.nus.edu.sg/handle/10635/57836||ISSN:||10834419||DOI:||10.1109/3477.826943|
|Appears in Collections:||Staff Publications|
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