Combining generative and discriminative strategies to facilitate efficient and effective learning from big data

随着数据量的持续快速增长，从海量数据存储中有效地提取信息变得越来越困难。很简单，从小数据中学习的有效技术不能扩展。然而，问题甚至比这更糟糕。大数据比小数据包含更多的信息，大多数最先进的学习算法都是在小数据中开发出来的。对于较小的数据，过于详细的分类器将过度适合数据，因此应该避免。相比之下，大数据提供了精细的细节，因此将使能够捕捉到它的新型学习者受益。这个项目将使学习者不仅能够捕捉到这一细节，而且能够以处理TB级数据所需的效率做到这一点。

Effective extraction of information from massive data stores is increasingly problematic as data quantities continue to grow rapidly. Quite simply, effective techniques for learning from small data do not scale. However, the problem is even worse than this. Big data contain more information than the small data in which context most state-of-the-art learning algorithms have been developed. For small data overly detailed classifiers will overfit the data and so should be avoided. In contrast, big data provide fine detail and hence will benefit new types of learner that can capture it. This project will deliver learners that are not only capable of capturing this detail, but do so with the efficiency required to process terabytes of data.