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Model-Based Multiple Instance Learning
Jul 6, 2017

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Title:

Model-Based  Multiple Instance Learning

Lecturer:

Prof. Ba-Ngu  Vo

Time:

2017-07-12 10:30:00

Venue:

North Campus West Building 106 Classroom

Lecturer  Profile

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. He is an  associate editor of the IEEE Transactions 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, systems  theory and stochastic geometry with emphasis on target tracking, space  situational awareness, robotics and computer vision.

Lecture 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 vector, in MI  learning each datum is a set or multi-set of unordered points. Despite a host  of techniques and applications as well as the fundamental role of statistical  models, MI learning based on statistical models have not yet been  investigated. This presentation discusses a framework for model-based MI  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 MI learning are developed.

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