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Our aim in this paper is to propose a rule-weight learning algorithm in fuzzy rule-based classifiers. The proposed algorithm is presented in two modes: first, all training examples are assumed to be equally important and the algorithm attempts to minimize the error-rate of the classifier on the training data by adjusting the weight of each fuzzy rule in the rule-base, and second, a weight is assigned...
A number of effective classification algorithms have been developed for spoken language recognition, and it has been a common practice in the NIST Language Recognition Evaluations (LREs) that an information fusion is applied to boost the performance of the recognition system. This paper investigates the fusion of multiple output scores generated using different classifiers that complement to further...
Fuzzy rule-based systems have been successfully used for pattern classification. These systems focus on generating a rule-base from numerical input data. The resulting rule-base can be applied on classification problems. However, we are faced with some challenges when generating and selecting the appropriate rules to create final rule-base. In this paper, a novel approach for rule selection is proposed...
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