Online Learning for Large Scale Structured Data in Complex Situations

在线学习是对一系列问题的答案进行预测的过程。由于其处理大规模非结构化数据和适应不断变化的环境的天然能力，OL近年来受到了极大的关注。然而，OL有三个弱点：它不能针对结构化数据进行扩展；它经常假设所有数据都是同等重要的；它经常认为所有数据都是完整的并且没有噪音。这些弱点限制了它的实用性，因为真实数据，如那些在处理社交网络时必须分析的数据，欺诈检测不满足这些限制。该项目的目的是发展理论和实践方面的进展，以克服现有的弱点。

Online Learning (OL) is the process of predicting answers for a sequence of questions. OL has enjoyed much attention in recent years due to its natural ability of processing large scale non-structured data and adapting to a changing environment. However, OL has three weaknesses: it does not scale for structured data; it often assumes that all of the data are equally important; it often considers that all of the data are complete and noise-free. These weaknesses limit its utility, because real data such as those that must be analysed in processing social networks, fraud detection do not satisfy the restrictions. The aim of this project is to develop theoretical and practical advances in OL that overcome the existing weaknesses.