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For building implementable and industry-valuable classification solutions, machine learning methods must focus not only on accuracy but also on computational and space complexity. We discuss a multistage method, namely cascading, where there is a sequence of classifiers ordered in terms of increasing complexity and specificity such that early classifiers(More)
We propose a multistage recognition method built as a cascade of a multi-layer perceptron (MLP) and a k-nearest neighbor (k-NN) classiier. MLP, being a distributed method, generalizes to learn a \rule" and the k-NN, being a local method, learns the localized \exceptions" rejected by the \rule." Because the rule-learner handles a large percentage of the(More)
|Learners based on diierent paradigms can be combined for improved accuracy. Each learning method assumes a certain model that comes with a set of assumptions which may lead to error if the assumptions do not hold. Learning is an ill-posed problem and with nite data each algorithm converges to a diierent solution and fails under diierent circumstances. Our(More)
|We propose a new method of classiication built as a cascade of a distributed learner and a local learner. The distributed learner generalizes to learn the \rule" and the local learner learns the \exceptions" not covered by the \rule." We show how such a system can be trained using cross-validation. We use a multi-layer perceptron with sigmoidal hidden(More)
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