A Novel Approach to Semi-Supervised Statistical Machine Learning

最近在构建用于诊断和预测的分类器方面取得了成功，部分原因是它们使用了大量按其原籍分类标记的数据。但通常情况下，有标签的数据很少，但有大量的未标签数据。半监督学习的目标是利用大量的未标记数据来提高性能，只使用较小的标记数据集，因此对于那些获得大量标记数据是昂贵或不切实际的应用来说，半监督学习是至关重要的。该项目旨在开发一种新的SSL方法，该方法采用针对缺失标签的缺失机制来构建分类器，该分类器不仅提高了精度，而且比已知缺失标签的情况下的分类精度更高。

Recent successes in the construction of classifiers for making diagnoses and predictions are due in part to their using much data labelled with respect to their class of origin. But typically there are little labelled data but plentiful unlabelled data. The goal of semi-supervised learning (SSL) is to leverage large amounts of unlabelled data to improve the performance using only small labelled datasets and so SSL is of paramount importance to applications where it is expensive or impractical to obtain much labelled data. The project is to develop a novel SSL approach that adopts a missingness mechanism for the missing labels to build a classifier that not only improves accuracy but it can be greater than if the missing labels were known.