Invited Speakers
INVITED TALK: TIN KAM HO
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Tin Kam Ho, Member of Technical Staff in the Computing Sciences Research Center, Bell Laboratories, and winner of the Pierre Devijver Award will present the Pierre Devijver lecture at S+SSPR 2008.
Bio: Tin Kam Ho leads the Statistics, Learning, and Computing Research Group of Bell Labs, Alcatel-Lucent at Murray Hill. Her interests are in methods and applications of pattern recognition, data mining, and computational modeling and simulation. She pioneered research in decision combination in multiple classifier systems, random decision forests, data complexity analysis, and many topics in image and text analysis. She has also led major efforts on modeling and monitoring large-scale optical transmission systems, and released the public software "Mirage" for interactive pattern discovery. Recently she worked on problems in user profiling, optical network diagnostics, and customer experience management. She is Editor-in-chief of the journal Pattern Recognition Letters, and elected Fellow of the International Association for Pattern Recognition and the IEEE. She has over 80 publications and 7 U.S. patents. She received a Ph.D. in Computer Science from Sunny Buffalo in 1992.
Title of Talk: Data Complexity Analysis: Linkage between Context and Solution in Classification
Abstract of Talk: For a classification problem that is implicitly represented by a training set, analysis of data complexity provides a linkage between context and solution. Instead of directly optimizing classification accuracy by tuning the learning algorithms, one may seek changes in the data sources and feature transformations to simplify the class geometry. Simplified class geometry benefits learning in a way common to many methods. We review some early results in data complexity analysis, compare these to recent advances in manifold learning, and suggest directions for further research.
INVITED TALK: HORST BUNKE
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Horst Bunke, Professor of the Computer Science Department at University of Bern, Switzerland is one of the invited speakers at the S+SSPR 2008 conference.
Bio:: Horst Bunke received his M.S. and Ph.D. degrees in Computer Science from the University of Erlangen, Germany. In 1984, he joined the University of Bern, Switzerland, where he is a professor in the Computer Science Department. He was Department Chairman from 1992 to 1996, Dean of the Faculty of Science from 1997 to 1998, and a member of the Executive Committee of the Faculty of Science from 2001 to 2003.
From 1998 to 2000 Horst Bunke served as 1st Vice-President of the International Association for Pattern Recognition (IAPR). In 2000 he also was Acting President of this organization. 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 received an honorary doctor degree from the University of Szeged, Hungary, and held visiting positions at the IBM Los Angeles Scientific Center (1989), the University of Szeged, Hungary (1991), the University of South Florida at Tampa (1991, 1996, 1998-2006), the University of Nevada at Las Vegas (1994), Kagawa University, Takamatsu, Japan (1995), Curtin University, Perth, Australia (1999), and Australian National University, Canberra (2005).
He served as a co-chair of the 4th Int. Conference on Document Analysis and Recognition held in Ulm, Germany, 1997 and as a Track Co-Chair of the 16th and 17th Int. Conference on Pattern Recognition held in Quebec City, Canada and Cambridge, UK in 2002 and 2004, respectively. Also he was chairman of the IAPR TC2 Workshop on Syntactic and Structural Pattern Recognition held in Bern 1992, a co-chair of the 7th IAPR Workshop on Document Analysis Systems held in Nelson, NZ, 2006, and a co-chair of the 10th Int. Workshop on Frontiers in Handwriting Recognition, held in La Baule, France, 2006. Horst Bunke was on the program and organization committee of many other conferences and served as a referee for numerous journals and scientific organizations. He is on the Scientific Advisory Board of the German Research Center for Artificial Intelligence (DFKI). Horst Bunke has more than 550 publications, including 36 authored, co-authored, edited or co-edited books and special editions of journals.
Title of Talk: Graph Classification on Dissimilarity Space Embedding
Abstract of Talk: Recently, an emerging trend of representing objects by graphs can be observed. In fact, graphs offer a powerful alternative to feature vectors in pattern recognition, machine learning, and related fields. However, the domain of graphs contains very little mathematical structure, and consequently, there is only a limited amount of classification algorithms available. In this paper we survey recent work on graph embedding using dissimilarity representations. Once a population of graphs has been mapped to a vector space by means of this embedding procedure, all classification methods developed in statistical pattern recognition become directly available. In an experimental evaluation we show that the proposed methodology of first embedding graphs in vector spaces and then applying a statistical classifier has significant potential to outperform classifiers that directly operate in the graph domain. Additionally, the proposed framework can be considered a contribution towards unifying the domains of structural and statistical pattern recognition.
(Joint work with: Kaspar Riesen)
INITED TALK: PEDRO DOMINGOS
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Pedro Domingos, Professor of the Computer Science Department at Washington State University is one of the invited speakers at the S+SSPR 2008 conference,
Bio: Pedro Domingos is Associate Professor of Computer Science and Engineering at the University of Washington. His research interests are in artificial intelligence, machine learning and data mining. He received a PhD in Information and Computer Science from the University of California at Irvine, and is the author or co-author of over 100 technical publications. He is a member of the advisory board of JAIR, a member of the editorial board of the Machine Learning journal, and a co-founder of the International Machine Learning Society. He was program co-chair of KDD-2003, and has served on numerous program committees. He has received several awards, including a Sloan Fellowship, an NSF CAREER Award, a Fulbright Scholarship, an IBM Faculty Award, and best paper awards at KDD-98, KDD-99 and PKDD-2005.
Title of Talk: Markov Logic: A Unifying Language for Structural and Statistical Pattern Recognition
Abstract of Talk: Effective pattern recognition requires understanding both statistical and structural aspects of the input, but in the past these have mostly been handled separately. Markov logic is a powerful new language that seamlessy combines the two. Models in Markov logic are sets of weighted formulas in first-order logic, interpreted as
templates for features of Markov random fields. Most statistical and structural models in wide use are simple special cases of Markov logic. Learning algorithms for Markov logic make use of conditional likelihood, convex optimization, and inductive logic programming. Inference algorithms combine ideas from Markov chain Monte Carlo and satisfiability testing. Markov logic has been successfully applied to problems in information extraction, robot mapping, social network modeling, and others, and is
the basis of the open-source Alchemy system.
(Joint work with: Stanley Kok, Daniel Lowd, Hoifung Poon, Matt Richardson, Parag Singla, Marc Sumner, and Jue Wang)
INVITED TALK: LUDMILA I. KUNCHEVA
Ludmila Kuncheva, Professor of the School of Computer Science, Bangor University, United Kingdom is one of the invited speakers at the S+SSPR 2008 conference.
Bio: Ludmila Kuncheva received the MSc degree from the Technical University, Sofia, in 1982, and the PhD degree from the Bulgarian Academy of Sciences in 1987. Until 1997 she worked at the Central Laboratory of Biomedical Engineering, Bulgarian Academy of Sciences as a Senior Research Associate. Dr Kuncheva is currently a Reader at the School of Computer Science, University of Bangor, UK. Her interests include pattern recognition and machine learning, in particular classifier combination. Dr Kuncheva has published above 100 research papers and two books (h-index 28). She won two best paper awards (2006 in IEEE Transactions on Fuzzy Systems and for 2003 across IEEE Transactions on SMC A, B and C) and has served as an AE for IEEE TFS and IEEE TPAMI. Dr Kuncheva has collaborated with colleagues from Spain, The Netherlands, Italy, Russia, Germany, Bulgaria, Lithuania, USA and Australia.
Title of Talk: Linear Discriminant Classifier (LDC) for Streaming Data with Concept Drift
Abstract of Talk: Simple classifiers, including LDC, have often been praised for their robustness and accuracy. Here we consider an online version of LDC applied to streaming data with concept drift. The classifier is trained on a moving window containing the latest N observations. Current approaches to determining the window size are mostly heuristical. The talk presents a framework within which theoretical relationship can be sought between the window size and the classification error. The framework is based upon two ideas. First, past literature offers formal relationships between the size of the training set and the asymptotic accuracy for several classifier models. Such a relationship can be used as a guide to determining the moving window size. Second, after a sudden change, the "old" classifier may be better than an undertrained "new" classifier that uses only the data coming after the change. We state the optimal window size for the case of LDC applied to two Gaussian classes and a sudden change in the form of rotation and translation of the feature space. Simulation results are included in order to investigate the sensitivity of the theoretical results to violation of the underlying assumptions.
(Joint work with: Indre Zliobaite)
