Abstract
Pattern recognition system on large sets of complex objects often have to deal with atypical samples that defy most traditional classifiers. Such samples can be handled with additional domain knowledge from an human expert. We propose a framework for the transfer of knowledge from the expert and incorporating it into the learning process of our recognition system using the rough mereology methods. We also show how this knowledge acquisition can be conducted in an interactive manner, with a large dataset of handwritten digits as an example.
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Nguyen, T.T. (2004). Domain Knowledge Approximation in Handwritten Digit Recognition. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_80
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DOI: https://doi.org/10.1007/978-3-540-25929-9_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22117-3
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