Graph-Based Semi-Supervised Learning

Speaker:	Dr. Dit-Yan YEUNG
		Associate Professor and Associate Head
		Department of Computer Science and Engineering
		The Hong Kong University of Science and Technology

Title:		"Graph-Based Semi-Supervised Learning"

Date:		Monday, 16 April 2007

Time:		4:00pm - 5:00pm

Venue:		Lecture Theatre F
		(Leung Yat Sing Lecture Theatre, near lift nos. 25/26)
		HKUST

Abstract:

For many decades, research in machine learning and pattern recognition has
focused on the supervised learning and unsupervised learning paradigms.
However, these paradigms are only the two extremes of a continuum of
possibilities.  Over the past decade, the machine learning research
community has witnessed significant interest in a new learning paradigm
called semi-supervised learning (SSL).  Increased interest in SSL is
partly due to the emergence of many applications in which unlabeled data
are plentiful but the labeling effort is very costly, such as many web,
image, speech, and bioinformatics applications.  This is an overview talk
on SSL for the general audience.  In particular, recent graph-based SSL
methods and applications, including some developed at HKUST, will be
presented.


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Biography:

Dit-Yan Yeung is an Associate Professor and Associate Head of the
Department of Computer Science and Engineering at the Hong Kong University
of Science and Technology.  He received his BEng degree in electrical
engineering and MPhil degree in computer science from the University of
Hong Kong, and PhD degree in computer science from the University of
Southern California.  His current research interests are in machine
learning and pattern recognition, particularly on semi-supervised
learning, embedding/manifold/spectral methods, kernel methods, as well as
their novel applications.