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Centroid estimation based on symmetric KL divergence for Multinomial text classification problem
August 29, 2018 Β· Declared Dead Β· π International Conference on Machine Learning and Applications
"No code URL or promise found in abstract"
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Authors
Jiangning Chen, Heinrich Matzinger, Haoyan Zhai, Mi Zhou
arXiv ID
1808.10261
Category
cs.IR: Information Retrieval
Cross-listed
cs.LG,
stat.ML
Citations
11
Venue
International Conference on Machine Learning and Applications
Last Checked
4 months ago
Abstract
We define a new method to estimate centroid for text classification based on the symmetric KL-divergence between the distribution of words in training documents and their class centroids. Experiments on several standard data sets indicate that the new method achieves substantial improvements over the traditional classifiers.
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