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

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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.