Combining multiple classifiers based on third-order dependency for handwritten numeral recognition

Authors:
Hee-Joong Kang
Affiliations:
Division of Computer Engineering, Hansung University, 389 Samsun-dong 2-ga, Sungbuk-gu, Seoul, South Korea
Venue:
Pattern Recognition Letters
Year:
2003

Citing 2
Cited 3

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IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal approximation of discrete probability distribution with kth-order dependency and its application to combining multiple classifiers

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Trainable fusion rules. I. Large sample size case

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Recognition of isolated handwritten Kannada numerals based on image fusion method

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A hybrid approach for automatic recognition of handwritten devanagari numerals

International Journal of Hybrid Intelligent Systems

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Abstract

Storing and estimating high order probability distribution of classifiers and class labels is exponentially complex and unmanageable, so we rely on an approximation scheme using the dependency. As an extension of the second-order dependency approach, the probability distribution is optimally approximated by the third-order dependency and then multiple classifiers are combined by such third-order dependency approximation.