【摘要】 文本自动分类是数据挖掘和机器学习中非常重要的研究领域 .针对难以获得大量有类标签的训练集问题 ,提出了基于小规模标注语料的增量式Bayes文本分类算法 .该算法分两种情况处理 :第一种情况是新增样本有类标签 ,可直接重新计算样本属于某类别的条件概率 .第二种情况是新增样本无类标签 ,则利用现有分类器为其训练类标签 ,然后利用新样本来修正分类器 .实验结果表明 ,该算法是可行有效的 ,比Na veBayes文本分类算法有更高的精度 .增量式Bayes分类算法的提出为分类器的更新提供了一条新途径更多还原

【Abstract】 Automatic text categorization is an important research field in data mining and machine learning. An incremental Bayes text categorization algorithm based on small labeled documents is presented to solve the difficult problem involving getting labeled training documents. The algorithm can process two cases: the labeled and unlabeled incremental documents. Directly computing the probability of the samples of a certain class is the processing method for labeled documents. The unlabeled documents are labeled first by using the original classification, and then the new classification is trained from the incremental documents. The experimental results show that this algorithm is feasible and effective, providing a new method for updating of classification.更多还原