题目：Model-Based Multiple Instance Learning

时间：2019年5月15日（周三）11：30

地点：阳明学堂

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内容简介：

This talk discusses a framework for model-based Multiple Instance (MI) learning using point process theory. While MI data are point patterns—sets or multi-sets of unordered points—appropriate statistical point pattern models have not been used in MI learning.  We proved the likelihood functions for point pattern data derived from point process theory enable principled yet conceptually transparent extensions of learning tasks, such as classification, novelty detection and clustering, to point pattern data. Furthermore, tractable point pattern models as well as solutions for learning and decision making from point pattern data are developed.

汇报人简介：

Prof. 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. Vo is a recipient of the Australian Research Council’s inaugural Future Fellowship and the 2010 Eureka Prize for Outstanding Science in support of Defense or National Security. Vo is best known as a pioneer in the stochastic geometric approach to multi-object system. His research interests are signal processing, systems theory and stochastic geometry with emphasis on target tracking, space situational awareness, robotics and computer vision.