摘要
借鉴聚类思想和万有引力计算方法 ,提出了解决基于示例学习中两个关键问题的新思路 .这两个新思路分别是 ,利用示例邻近同类其它示例数目来描述该示例潜在预测能力 ,以及利用实例质量来帮助更加准确地预测新实例类别 .据此构造了一种聚类型基于示例学习的新方法 ,并利用标准机器学习数据库中 3个复杂数据样本 ,对所提方法的性能进行实验检测 .有关的对比实验结果表明 ,所提方法在实例预测能力以及学习结果占用空间有效性方面 ,均优越其它多种基于示例学习方法 .
By combining the clustering idea with the law of gravity in physics, two new ideas are put forward to solve two important problems. One uses the number of the neighbor instances around one instance to describe its potential power and the other uses instance quality to predict a new instance class. Based on those two new ideas, a new approach to instance based learning using clustering is constructed. The results of the comparative experiment made on three complex data sets from the machine learning library show that this new approach outperforms many other instance based learning methods in aspects of the prediction power and the learning result memory requirement.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2000年第11期1293-1297,共5页
Journal of Computer Research and Development
基金
安徽省自然科学基金资助!(项目编号 98312 82 0 )
关键词
示例学习
监督学习
机器学习
聚类
instance based learning, supervised learning, machine learning
