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

结合生成策略和判别策略，促进大数据高效学习

• 批准号: DP140100087
• 负责人: Prof Geoffrey Webb
• 金额: $ 66.92万
• 依托单位: Monash University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2014
• 资助国家: 澳大利亚
• 起止时间: 2014-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP140100087
• 关键词: Combining; generative; discriminative; strategies; facilitate

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.

随着数据量持续快速增长，从海量数据存储中有效提取信息越来越成问题。很简单，从小数据中学习的有效技术不能扩展。然而，问题比这更糟糕。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的背景下开发的。对于小数据，过于详细的分类器会过拟合数据，因此应该避免。相比之下，大数据提供了精细的细节，因此将有利于能够捕获它的新型学习者。该项目将提供不仅能够捕获这些细节的学习者，而且能够处理TB数据所需的效率。