Abstract
This paper presents a methodology to generate representations for isolated handwritten symbols, modeled as a multi-objective optimization problem. We detail the methodology, coding domain knowledge into a genetic based representation. With the help of a model on the domain of handwritten digits, we verify the problematic issues and propose a hybrid optimization algorithm, adapted to needs of this problem. A set of tests validates the optimization algorithm and parameter settings in the model’s context. The results are encouraging, as the optimized solutions outperform the human expert approach on a known problem.
Keywords
- Objective Space
- Handwritten Digit
- Feature Subset Selection
- Projection Distance
- Handwritten Digit Recognition
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Radtke, P.V.W., Wong, T., Sabourin, R. (2005). A Multi-objective Memetic Algorithm for Intelligent Feature Extraction. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31880-4_53
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DOI: https://doi.org/10.1007/978-3-540-31880-4_53
Publisher Name: Springer, Berlin, Heidelberg
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