Theory and algorithms for semi-supervised learning

研究者从决策理论的角度研究半监督学习。研究表明，在贝叶斯框架中，应该使用未标记的数据来构建先验，以提高预测学习的效果。更一般地说，研究者考虑了从未标记数据的假设空间构建先验和学习预测结构的问题。在这个统一的框架下，研究者系统地研究了半监督学习的理论和算法后果。统计机器学习涉及构建计算机系统，该系统可以根据观察到的信息（数据）预测未观察到的信息（标签）。例如，根据血液检查（数据）来预测患者是否患有癌症（标签）。传统上，统计机器学习算法从一组标记数据中构建预测规则。统计机器学习的实际应用中最重要的问题之一是是否可以通过使用未标记的数据来提高学习算法的性能。这是因为未标记的数据通常是丰富的，而他们的标签是非常昂贵的获取。同时使用标记和未标记数据的方法通常被称为半监督学习。本研究试图为半监督学习建立一个通用的统计理论，并应用该理论来改进最先进的机器学习算法。

The investigator studies semi-supervised learning from a decision theoretical point of view. The research shows that in the Bayesian framework, unlabeled data should be used to construct a prior for the purpose of improving predictive learning. More generally, the investigator considers the problem of constructing priors and learning predictive structures on hypothesis spaces from unlabeled data. Under this unified framework, the investigator systematically studies theoretical and algorithmic consequences of semi-supervised learning. Statistical machine learning is concerned with building computer systems that can predict unobserved information (labels) based on observed information (data). For example, to predict whether a patient has cancer (label) based on blood test (data). Traditionally, a statistical machine learning algorithm builds prediction rules from a set of labeled data. One of the most important issues in practical applications of statistical machine learning is whether one can improve the performance of a learning algorithm by using unlabeled data. This is because unlabeled data are generally abundant while their labels are very costly to obtain. Methods that use both labeled and unlabeled data are generally referred to as semi-supervised learning. This research attempts to establish a general statistical theory for semi-supervised learning, and applies the theory to improve state of the art machine learning algorithms.