Swarm intelligence
Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
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This paper addresses the issue of class-related reject thresholds for cascading classifier systems. It has been demonstrated in the literature that class-related reject thresholds provide an error-reject trade-off better than a single global threshold. In this work we argue that the error-reject trade-off yielded by class-related reject thresholds can be further improved if a proper algorithm is used to find the thresholds. In light of this, we propose using a recently developed optimization algorithm called Particle Swarm Optimization. It has been proved to be very effective in solving real valued global optimization problems. In order to show the benefits of such an algorithm, we have applied it to optimize the thresholds of a cascading classifier system devoted to recognize handwritten digits.