SVM Classifier Estimation from Group Probabilities
SVM Classifier Estimation from Group Probabilities
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发表时间：
2010-06
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通讯作者：
S. Rüping
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作者：
S. Rüping
最近才在机器学习社区引起关注的一个学习问题是从组概率中学习分类器。这是一项介于监督学习和无监督学习之间的学习任务，从某种意义上说，对于一组观察，我们不知道标签，但对于某些观察组，标签的频率分布是已知的。这个学习问题具有重要的实际应用，例如在保护隐私的数据挖掘中。本文提出了一种基于支持向量回归和分类器校准过程反转思想的从群概率中学习分类器的方法。详细的分析将表明，这种新方法优于现有的方法。
A learning problem that has only recently gained attention in the machine learning community is that of learning a classifier from group probabilities. It is a learning task that lies somewhere between the well-known tasks of supervised and unsupervised learning, in the sense that for a set of observations we do not know the labels, but for some groups of observations, the frequency distribution of the label is known. This learning problem has important practical applications, for example in privacy-preserving data mining. This paper presents an approach to learn a classifier from group probabilities based on support vector regression and the idea of inverting a classifier calibration process. A detailed analysis will show that this new approach outperforms existing approaches.