Support Vector Machine Learning from Positive and Unlabeled Samples

摘要：

In many machine learning settings,labeled samples are difficult to collect while unlabeled samples are abundant We investigate in this paper the design of support vector machine classification algorithms learning from positive and unlabeled samples only.Wefirst find the minimum bounding sphere that enclosed all the positive samples,and then use this minimum bounding sphere to pick out the negative samples from the unlabeled samples,at last we train the support vector machine using the training set which consists of the given positive samples and the negative samplespicked out from the unlabeled samples.Experiments indicate that support vector machine learning frompositive and unlabeled samples achieves the desiredhigh test precision and prediction accuracy.

作者: Ai-bing Ji Qi-ming Niu Ming-hu Ha

作者单位: College of Medicine,Hebei University,Baoding 071000,Hebei Peoples Republic of China College of Mathematics and Computer,Hebei University,Hebei Peoples Republic of China

会议类型: 国际会议

会议名称: 2008 3rd International Conference on Intelligent System and Knowledge Engineering(第三届智能系统与知识工程国际会议)(ISKE 2008)

会议地点: 厦门

会议语种:英文

页码: 978-982

在线出版日期: 2008-11-17（万方平台首次上网日期，不代表论文的发表时间）