摘要
在机器学习中 ,主动学习具有很长的研究历史 .给出了主动贝叶斯分类模型 ,并讨论了主动学习中几种常用的抽样策略 .提出了基于最大最小熵的主动学习方法和基于不确定抽样与最小分类损失相结合的主动学习策略 ,给出了增量地分类测试实例和修正分类参数的方法 .人工和实际的数据实验结果表明 。
The fundamental notion of active learning has a long history in machine learning. In this paper, an active Bayesian net model is provided and several common methods for sampling are given. Then two strategies for active learning are discussed: one is the method based on maximizing and minimizing entropy, and the other is the method combining the uncertainty sampling and minimizing classification loss. Meanwhile, also given is the method that classifies the example and update model parameters incrementally. Artificial and practical experiments show that the active learning methods proposed have high precision and recalls in a few examples with the class label.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2002年第5期574-579,共6页
Journal of Computer Research and Development
基金
国家自然科学基金资助 ( 6980 30 10
69790 0 80
60 0 730 19)
