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In this paper, an incremental framework for feature selection and Bayesian classification for multivariate normal distribution is proposed. Feature set can be determined incrementally using Kullback divergence and Chernoff distance measures which are commonly used for feature selection. The proposed integrated incremental learning is computationally efficient over its batch mode in terms of time....
Discriminant function is commonly and effective methodology for solving classification problems. However, it is computationally efficient when all features are considered simultaneously. But sometimes all the features do not contribute significantly to classification. Also the noisy attributes sometimes may decrease the accuracy of classifier. So before classification feature selection is used as...
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