Resource-bounded adaptive inference of accurate conditional probability estimates from data

该项目将开发具有有价值的新功能的机器学习技术：根据可用计算资源的限制，以不同程度的预期精度对复杂条件概率进行估计的能力。这将为许多类型的在线应用程序的开发人员提供显著的竞争优势，允许他们在从数据进行推断时最大限度地利用可用的计算资源，同时在系统设计期间灵活地权衡准确性和计算资源。澳大利亚也将受益于加强其机器学习专业知识，这是许多复杂和智能系统以及蓬勃发展的数据挖掘行业的核心。

This project will develop machine learning techniques with a valuable new capability: the ability to produce estimates of complex conditional probabilities to varying levels of expected accuracy depending upon the constraints of available computational resources. This will provide significant competitive advantage to developers of many types of online application by allowing them to maximise utilisation of available computational resources when making inferences from data, together with the flexibility to trade-off accuracy and computing resources during system design. Australia will also benefit by strengthening its machine learning expertise, which is central to many complex and intelligent systems and the booming data mining industry.