Semi-supervised learning with graphs
Semi-supervised learning with graphs
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发表时间：
2005
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通讯作者：
Xiaojin Zhu;J. Lafferty;R. Rosenfeld
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作者：
Xiaojin Zhu;J. Lafferty;R. Rosenfeld
在传统的机器学习分类方法中，只使用标记集来训练分类器。然而，标记的实例通常是困难的，昂贵的，或者是耗时的，因为它们需要有经验的人类注释者的努力。与此同时，未标记的数据可能相对容易收集，但使用它们的方法很少。半监督学习通过使用大量未标记数据和标记数据来构建更好的分类器来解决这个问题。由于半监督学习需要更少的人力和更高的准确性，它在理论和实践中都有很大的兴趣。我们提出了一系列新的半监督学习方法所产生的图形表示，标记和未标记的实例表示为顶点，边编码的实例之间的相似性。他们解决了以下问题：如何使用未标记的数据？（标签传播）;概率解释是什么？（高斯场和调和函数）;如果我们可以选择标记数据呢？（主动学习）;如何构造好的图形？（超参数学习）;如何使用像SVM这样的核机器？（图核）;如何处理像序列这样的复杂数据？（内核条件随机场）;如何处理可扩展性和归纳？（harmonic mixtures）。一个广泛的文献综述包括在最后。
In traditional machine learning approaches to classification, one uses only a labeled set to train the classifier. Labeled instances however are often difficult, expensive, or time consuming to obtain, as they require the efforts of experienced human annotators. Meanwhile unlabeled data may be relatively easy to collect, but there has been few ways to use them. Semi-supervised learning addresses this problem by using large amount of unlabeled data, together with the labeled data, to build better classifiers. Because semi-supervised learning requires less human effort and gives higher accuracy, it is of great interest both in theory and in practice. We present a series of novel semi-supervised learning approaches arising from a graph representation, where labeled and unlabeled instances are represented as vertices, and edges encode the similarity between instances. They address the following questions: How to use unlabeled data? (label propagation); What is the probabilistic interpretation? (Gaussian fields and harmonic functions); What if we can choose labeled data? (active learning); How to construct good graphs? (hyperparameter learning); How to work with kernel machines like SVM? (graph kernels); How to handle complex data like sequences? (kernel conditional random fields); How to handle scalability and induction? (harmonic mixtures). An extensive literature review is included at the end.