题目：Recent Advances in Graph Based Pattern Recognition 报告人：Professor Horst Bunke，University of Bern, Switzerland 时间：10月24日14:00-15:00
地点：蒙民伟楼404 摘要：The discipline of pattern recognition is traditionally divided into
the statistical and the structural approach. Statistical pattern recognition
is characterized by representing objects by means of feature vectors, while
the structural approach uses symbolic data structures, such as strings,
trees, and graphs. In the current talk we mainly focus on graphs for object
representation. When comparing graph representations with feature vectors,
one notices an increased flexibility provided by graphs in the sense that
not only unary properties of objects can be represented, but also
higher-dimensional relationships. On the other hand, the domain of graphs
exhibits only little mathematical structure and consequently, there is a
lack of suitable methods for graph classification, clustering, and related
tasks.
In this talk, we review some advances in the field of graph-based pattern
recognition. The main focus will be on work conducted at the University of
Bern in recent years. A general approach to transforming graphs into
n-dimensional real vectors by means of graph dissimilarity will be
described. This approach makes all algorithmic tools originally developed
for feature vectors instantly available to graphs. In particular, it leads
to a novel family of graph kernels. Moreover, the embedding procedure allows
one to generate, in a straightforward way, systems for classification and
clustering that involve multiple experts. With various experimental results
we prove the robustness and flexibility of this new approach and show that
it outperforms standard graph classification and clustering methods on
several graph data sets of diverse nature. In order to address computational
complexity problems, we also describe novel algorithms for the efficient
computation of graph dissimilarity in an approximate way. It will be shown
that these algorithms can substantially reduce computation time while
maintaining a high accuracy of the underlying classification algorithms. 简介：Horst Bunke is a professor of Computer Science at the University of
Bern, Switzerland. From 1998 to 2000 he served as the 1st Vice-President of
the International Association for Pattern Recognition (IAPR) and in 2000 he
was Acting President of IAPR. Horst Bunke is a Fellow of the IAPR, former
Editor-in-Charge of the International Journal of Pattern Recognition and
Artificial Intelligence, Editor-in-Chief of the journal Electronic Letters
of Computer Vision and Image Analysis, Editor-in-Chief of the book series on
Machine Perception and Artificial Intelligence by World Scientific Publ.
Co., Advisory Editor of Pattern Recognition, Associate Editor of Acta
Cybernetica and Frontiers of Computer Science in China, and former Associate
Editor of the International Journal of Document Analysis and Recognition,
and Pattern Analysis and Applications.
Horst Bunke is the recipient of the 2010 KS Fu Prize, awarded by the IAPR.
Moreover, he received the IAPR/ICDAR Outstanding Achievements Award in 2009
and an honorary doctor degree from the University of Szeged, Hungary, in
2007. He held visiting positions at the Chinese Academy of Science, Beijing
(1987), the IBM Los Angeles Scientific Center (1989), the University of
Szeged, Hungary (1991), the University of South Florida at Tampa (1991,
1996, 1998-2007), the University of Nevada at Las Vegas (1994), Kagawa
University, Takamatsu, Japan (1995), Curtin University, Perth, Australia
(1999), Australian National University, Canberra (2005), Autonomous
University, Barcelona (2005), and NICTA, Brisbane (2009). Horst Bunke has
more than 650 publications, including over 40 authored, co-authored, edited
or co-edited books and special editions of journals. His h-index is 44, as
determined by Google Scholar and harzing.com software. In the DBLP Computer
Science Bibliography, which captures more than 1.4 million papers from the
whole discipline of Computer Science, Horst Bunke ranks among the 60 most
prolific authors in the Computer Science community.
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