摘要：

样本标记是一个重要但又比较耗时的过程。得到一个多标签分类器需要大量的训练样本，而手工为每个样本创建多个标签会存在一定困难。为尽可能降低标记样本的工作量，提出一种加权决策函数的主动学习方法，该方法同时考虑训练样本的数量和未知样本的置信度，使得分类器能在最小的成本下最快地达到比较满意的分类精度。

关键词: 主动学习, 多标签, 支持向量机, 训练样本

Abstract:

Manually creating multiple labels for each sample is very important but it is time-consuming. Manually creating multiple labels for each sample may become impractical when a very large amount of data is needed for training multi-label classifier. To minimize the human-labeling efforts, this paper proposes a weighted decision approach, the approach considers quantity and confidence of training samples, it can make the classifier need fewer samples, but achieve a comparative precision.

Key words: active learning, multi-label, Support Vector Machine(SVM), training samples

中图分类号: 

• TP18