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

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.