Machine learning is a critical enabling technology that underlies many advanced developments in commerce, industry, governance and science. This project investigates a new paradigm for machine learning that learns without search. This new paradigm will be used to develop new probabilistic learning techniques that address limitations of the current state-of-the-art. These novel learning algorithms will create more accurate models of categorical data, will be robust in the presence of data errors, will support incremental learning and will be well suited to the types of very large data that are becoming increasingly common. This will allow more accurate models to be learnt from these common types of data than has previously been possible.
Chen, S., Martinez, A. & Webb, G. I. B., 2014, Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, Proceedings (PAKDD 2014), Part II. Tseng, V. S., Ho, T. B., Zhou, Z-H., Chen, A. L. P. & Kao, H-Y. (eds.). Cham Switzerland: Springer, p. 86 - 9712 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review