報告題目：Contextual probability and neighbourhood counting (語境概率及鄰域計算)

報告時間：2022年5月28日（星期六）20:00-21:30

報告地點(diǎn)：線上騰訊會議：377-875-686

報告內(nèi)容簡介：

In this talk, Prof. Wang will present the concept of contextual probability, the resulting notion of neighbourhood counting and the various specialisations of this notion which result in new functions for measuring similarity. Contextual probability was originally proposed as an alternative way of uncertainty reasoning. It was later found to be a perceptionist approach to estimating probability by systematically perceiving the true probability distribution through neighbourhoods. As a result, the true probability can be approximated by neighbourhood counting, which leads to a kernel function and a generic similarity metric that can be applied to different types of data.

報告人簡介：

Dr Hui Wang is Professor of Artificial Intelligence and Theme Lead of Artificial Intelligence in School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast. Prior to joining Queen’s, he was Professor of Computer Science in the School of Computing, Ulster University, in various roles including Head of the AI Research Centre and Research Director. He is an associate editor of IEEE Transactions on Cybernetics, founding Chair of IEEE SMCS Northern Ireland Chapter (2009-2018), and a member of IEEE SMCS Board of Governors (2010-2013).

His research interests are machine learning, knowledge representation and reasoning, combinatorial data analytics, and their applications in image, video, spectra and text data analyses. He played an important role in the development of an algebraic framework for machine learning and knowledge representation, Lattice Machine; the original concept of contextual probability for probabilistic modelling, which can be used for uncertainty reasoning/quantification, probability estimation and machine learning; a generic similarity measure, neighbourhood counting, and its specialisations on multivariate data, sequences, tree and graph structures; and more recently detection learning and knowledge-based learning. He has over 300 publications in these areas.

He is principal investigator of a number of regional, national and international projects in the areas of image/video analytics (EPSRC funded MVSE 2021-2024, EU Horizon 2020 funded DESIREE and ASGARD, EU FP7 funded SAVASA, Royal Society funded VIAD), spectral data analytics (EPSRC funded VIPIRS on virus detection 2020-2022), text analytics (INI funded DEEPFLOW, Royal Society funded BEACON), and intelligent content management (EU FP5 funded ICONS); and is co-investigator of several other EU funded projects.