S\u00e9minaire Tipic pr\u00e9sent\u00e9 par le Pr. Ba-Ngu Vo, le 28\/06\/2018 \u00e0 14h00 en F208 \u00e0 TSP<\/p>\n
Title:<\/strong> Model-Based Learning for Point Patterns<\/p>\n Abstract:<\/strong><\/p>\n Stemming from research on handwritten digit recognition in 1990, Multiple instance (MI) learning has emerged as an important topic in Machine Learning. Unlike conventional Machine Learning problems where each datum is a vector, in MI learning each datum is a point pattern or multi-set of unordered points. Despite a host of techniques and applications as well as the fundamental role of statistical models, learning for point pattern data based on statistical models have not yet been investigated. This presentation discusses a framework for model-based point pattern learning using point process theory, which enables principled yet conceptually transparent extensions of learning tasks, such as classification, novelty detection and clustering. Furthermore, tractable point process models as well as solutions for point pattern learning are developed.<\/p>\n Biography:<\/strong><\/p>\n Ba-Ngu Vo received his Bachelor degrees in Pure Mathematics and Electrical Engineering with first class honours in 1994, and PhD in 1997. Currently he is Professor and Chair of Signals and Systems in the Department of Electrical and Computer Engineering at Curtin University, Australia. Vo is a recipient of the Australian Research Council\u2019s inaugural Future Fellowship and the 2010 Eureka Prize for Outstanding Science in support of Defense or National Security. He is an associate editor of the IEEE Transaction on Aerospace and Electronic System. Vo is best known as a pioneer in the stochastic geometric approach to multi-object system. His research interests are signal processing and stochastic geometry with emphasis on target tracking, space situational awareness, and computer vision.<\/p>\n","protected":false},"excerpt":{"rendered":" S\u00e9minaire Tipic pr\u00e9sent\u00e9 par le Pr. Ba-Ngu Vo, le 28\/06\/2018 \u00e0 14h00 en F208 \u00e0 TSP Title: Model-Based Learning for Point Patterns Abstract: Stemming from research on handwritten digit recognition in 1990, Multiple instance (MI) learning has emerged as an important topic in Machine Learning. Unlike conventional Machine Learning problems where each datum is a 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