| Lecturer Profile |
Witold Pedrycz is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. He also holds an appointment of special professorship in the School of Computer Science, University of Nottingham, UK. In 2009, Dr. Pedrycz was elected a foreign member of the Polish Academy of Sciences. In 2012 he was elected a Fellow of the Royal Society of Canada. Witold Pedrycz has been a member of numerous program committees of IEEE conferences in the area of fuzzy sets and neurocomputing. He has received several awards such as Killam Prize in 2013. His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery and data mining, fuzzy control, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published numerous papers in this area. He is also an author of 15 research monographs covering various aspects of Computational Intelligence, data mining, and Software Engineering.Dr. Pedrycz is intensively involved in editorial activities. He is an Editor-in-Chief and Co-Editor-in Chief of several international journals or conferences. |
| Lecture Abstract |
Mining of multimedia data (streams), often related with a comprehensive characterization and detection of events of interest, becomes a challenging endeavor. The complexity of such tasks of mining is implied by the inherent diversity of the involved modalities of the collected data and a semantically rich nature of the undertaken pursuits. It becomes apparent that human-centric aspects of the mining activities have to be taken into account. In essence, data, events and relationships between them are comprehended and described with the aid of some semantically sound entities – information granules.
The proposed concepts and ensuing architecture are hierarchical and comprise three key level of the hierarchy. The concepts could be defined in advance and afterwards might be subject to learning and further refinements. At the highest level of the hierarchy, relationships among the concepts are determined and a formation of events and their temporal and spatial dependencies is discovered. It is demonstrated that the hierarchical processing realized in a form outlined above leads in a natural way to the emergence of information granules of higher type and higher order.
To make the presentation self-contained, a concise introduction to the principles and the development methodology of Granular Computing is provided. |
