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

根据数据精确条件概率估计的资源有限自适应推理

• 批准号: DP0556279
• 负责人: Prof Geoffrey Webb
• 金额: $ 17.13万
• 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: 2005
• 资助国家: 澳大利亚
• 起止时间: 2005-01-27 至 2008-01-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP0556279
• 关键词: Resource; bounded; adaptive; inference; accurate

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.

该项目将开发具有有价值的新能力的机器学习技术：根据可用计算资源的限制，以不同的预期准确度水平产生复杂条件概率的估计的能力。 这将为许多类型的在线应用程序的开发人员提供显着的竞争优势，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，以及在系统设计期间权衡准确性和计算资源的灵活性。 澳大利亚还将通过加强其机器学习专业知识而受益，这是许多复杂智能系统和蓬勃发展的数据挖掘行业的核心。