Location: IGI-seminar room,
Inffeldgasse 16b/I, 8010 Graz Date: starting from
Oct. 10, 2005 every Monday, 16.15
- 18.00 p.m.
Content of the seminar:
This Seminar will examine the current state of the art in nonlinear
time series analysis, with an emphasis on methods from machine learning
(and an emphasis on classification, indexing, clustering,
dimension-reduction, and pattern extraction; rather than times
series prediction).
In particular, we will discuss of methods for defining a suitable
metric on time series, kernel based methods, neural net based
methods, methods of dynamic
bayesian networks, and methods for benchmarking such algorithms. This
seminar will be carried out jointly with Horst Bischof and Gernot
Kubin, since one goal of this seminar is to examine learning methods
that
appear to be relevant for ongoing research in the MISTRAL
project. This is a joint research project of many computer science
institutes at
our university. (MISTRAL stands for Measurable intelligent and secure
semantic
extraction and retrieval of
multimedia data).
Talks:
Talks will be assigned at our first meeting on Oct. 10.
Talks will probably have a length of about 40 minutes.
Schedule of talks:
21.11.2005
Malte Rasch (?) - "Probabilistic Discovery of Time Series
Motifs"
B Chiu, E Keogh, S Lonardi - the 9th ACM SIGKDD International
Conference on Knowledge, 2003 http://www.cs.ucr.edu/~eamonn/SIGKDD_Motif.pdf
Gerhard Neumann - "Gaussian Process Dynamical Models"
Jack M. Wang,
David J.
Fleet, Aaron
Hertzmann www.dgp.toronto.edu/~jmwang/gpdm/
Presentation:: (PPT)
28.11.2005
Andreas Juffinger - "On the Need for Time Series Data Mining
Benchmarks:
A Survey and
Empirical
Demonstration"
E Keogh, S Kasetty - Data Mining and Knowledge Discovery,
2003 http://www.cs.ucr.edu/~eamonn/Data_Mining_Journal_Keogh.pdf
Stefan Häusler - "Making Time-series Classification More Accurate
Using Learned
Constraints"
CA Ratanamahatana, E Keogh - proceedings of SIAM International
Conference on Data Mining,
2004
http://www.cs.ucr.edu/~ratana/sdm04.pdf.gz
Marian Kepesi - "Bayesian filtering + applications of Bayesian methods
in speech recognition for location estimation"
D. Fox, J. Hightower, L. Liao, D. Schulz, and G. Borriello,
"Bayesian filtering for location estimation," IEEE Pervasive Computing,
2(3):24-33, July-September 2003. http://www.cs.washington.edu/ai/Mobile_Robotics/postscripts/bayes-location-ubicomp-03.pdf
09.01.2006
Ashley Mills - "Learning Dynamic Bayesian
Networks"
Z Ghahramani - Summer School on Neural Networks,
1997
Presentation: (PPT)
23.01.2006
David Sussillo on material from
"Kernel methods for time series classification"
Using support vector machines for time series
prediction,
Klaus-Robert Müller, Alexander J. Smola , G Raetsch, B
Schoekopf, J Kohlmorgen, V
Vapnik
Advances in kernel methods: support vector learning table,
1999 http://portal.acm.org/citation.cfm%3Fid%3D299094.299107
Martin Bachler on material from
Kevin Murphy, "Dynamic Bayesian Networks: Representation,
Inference and Learning"
PhD Thesis, UC Berkeley, Computer Science Division, July 2002. http://citeseer.ist.psu.edu/context/2157172/0