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In this paper an ensemble model is proposed for the recognition of Odia handwritten character. The ensemble model is constructed from four base classifiers: Support Vector Machine (SVM), Artificial Neural Network (ANN), C5.0 Decision Tree and Discriminant Analysis (DA). Gradient and curvature based features are extracted from the numerals and a combination of gradient and curvature based features...
Hybrid of neural network (NN) and hidden Markov model (HMM) has been popular in word recognition, taking advantage of NN discriminative property and HMM representational capability. However, NN does not guarantee good generalization due to empirical risk minimization (ERM) principle that it uses. In our work, we focus on using the support vector machine (SVM) for character recognition. SVM's use of...
This paper proposes a novel hierarchical speaker identification method to save the speaker identification and training time, viz. First is to get a coarse decision by a fast scan all registered speakers using PCA classifier to found M possible target speakers; then is to get a final decision by the proposed Multi-Reduced Support Vector Machine (MRSVM). And the MRSVM has two reduction steps to reduce...
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