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On the Use of Pairwise Comparison of Hypotheses in Evolutionary Learning Applied to Learning from Visual Examples

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2123))

Abstract

This paper is devoted to the use of genetic programming for the search of hypothesis space in visual learning tasks. The long-term goal of our research is to synthesize human-competitive procedures for pattern discrimination by means of learning process based directly on the training set of images. In particular, we introduce a novel concept of evolutionary learning employing, instead of scalar evaluation function, pairwise comparison of hypotheses, which allows the solutions to remain incomparable in some cases. That extension increases the diversification of the population and improves the exploration of the hypothesis space search in comparison with ‘plain’ evolutionary computation using scalar evaluation. This supposition is verified experimentally in this study in an extensive comparative experiment of visual learning concerning the recognition of handwritten characters.

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Author information

Authors and Affiliations

  1. Institute of Computing Science, Poznań University of Technology, Piotrowo 3A, 60965, Poznań, Poland

    Krzysztof Krawiec

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  1. Krzysztof Krawiec
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Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Arno-Nitzsche-Str. 45, 04277, Leipzig, Germany

    Petra Perner

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Krawiec, K. (2001). On the Use of Pairwise Comparison of Hypotheses in Evolutionary Learning Applied to Learning from Visual Examples. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2001. Lecture Notes in Computer Science(), vol 2123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44596-X_25

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  • DOI: https://doi.org/10.1007/3-540-44596-X_25

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42359-1

  • Online ISBN: 978-3-540-44596-8

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