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Measuring similarity or distance plays a key role for data mining and knowledge discovery tasks. A lot of work has been performed on continuous attributes, but for nominal attributes the similarity computation is not relatively well-understood. In this paper, we propose a novel approach to learn a family of dissimilarity measures for categorical data. Based on these measures distance between two different...
This paper addresses the ongoing issue of tone error detection for Mandarin Computer Assisted Language Learning (CALL) systems. A novel approach based on clustering is proposed. The selection of different contextual tonal factors including Uni-tone, LBi-tone and RBi-tone are explored. Experimental results show that our proposed approach is feasible, obtaining an Equal Error Rate (EER) of 18.75% by...
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