Abstract
Handwritten Gestures may vary from person to person. Moreover, they may vary for same person, if taken at different time and mood. Various rule-based automatic classifiers have been designed to recognize handwritten gestures. These classifiers generally include new rules in rule set for unseen inputs, and most of the times these new rules are distinguish from existing one. However, we get a huge set of rules which incurs problem of over fitting and rule base explosion. In this paper, we propose a self adaptive gesture fuzzy classifier which uses maximum entropy principle for preserving most promising rules and removing redundant rules from the rule set, based on interestingness. We present experimental results to demonstrate various comparisons from previous work and the reduction of error rates.
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Malhotra, R., Srivastava, R., Bhartee, A.K., Verma, M. (2013). Self-adaptive Gesture Classifier Using Fuzzy Classifiers with Entropy Based Rule Pruning. In: Abraham, A., Thampi, S. (eds) Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32063-7_24
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DOI: https://doi.org/10.1007/978-3-642-32063-7_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32062-0
Online ISBN: 978-3-642-32063-7
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