Abstract
This paper proposes a small sample text classification algorithm based on kernel density estimation. Firstly, the probability density of the text classification problem is estimated. Then we can construct auxiliary training samples by the estimated probability. Finally, the classification model is obtained with the help of auxiliary training samples. As the introduction of auxiliary training samples avoids over-fitting caused by small training samples, the proposed algorithm can effectively improve the performance of small sample text classification problems. The simulation experiments on the news text datasets fully verify the effectiveness of the proposed algorithm.
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