Contents

• Introduction
• Information distribution
• Pierre Devijver Award

  The 2004 Pierre Devijver Award winner is Erkki Oja

• Membership list
• Data sets for benchmarking and comparative studies
• Workshops, conferences and upcoming events

Introduction

Topics of interest for IAPR-TC1 mainly concern:

• classical statistical PR methods,
• feature selection and extraction,
• classifier design and testing,
• small sample size problems,
• clustering techniques,
• contextual methods,
• comparing and combining PR methods,
• artificial neural networks,

• to facilitate establishing of closer contacts among all the researchers working in various directions of statistical techniques either within or outside the current PR community
• to create and maintain a membership list
• to inform about forthcoming conferences and workshops of likely interest to the members
• to produce a White Book which will inform about the activities of the members worldwide
• to organize regularly workshops on statistical techniques in PR

Information distribution

Pierre Devijver Award

On request of the IAPR the Technical Committee on Statistical Pattern Recognition (TC1) established the a special award to commemorate Pierre Devijver, one of the founders of statistical pattern recognition, who left us all in 1996. It was decided to set up a Pierre Devijver Award Lecture, to be presented at regular TC1 workshops . The presenter would be selected from among outstanding scientists who had contributed significantly to the field of statistical pattern recognition.

• The 2004 Pierre Devijver Award winner is Erkki Oja
• more information ...

Membership list

• search the TC1 member list

Data sets for benchmarking and comparative studies

TC1 has the intention to collect pointers to existing public data sets that may be useful for benchmarking and comparative studies. During the MCS 2001 on combining classifiers the participants agreed to focus in particular on data sets and protocols suitable for comparing multiple classifier systems. Pointers to suggested material will be made available here later.

• A general archive for many suitable data sets is the UCI Repository of Machine Learning Databases and Domain Theories

• Z. Zheng, A benchmark for classifier learning. Proceedings of the 6th Australian Joint Conference on Artificial Intelligence, Singapore: World Scientific, 281-286, 1993. [ai93-benchmark.ps.gz, 54kB]
• Lutz Prechelt. PROBEN1 -- A Set of Benchmarks and Benchmarking Rules for Neural Network Training Algorithms. [1994-21.ps.gz]. Technical Report 21/94, 38 pages, Fakultät für Informatik, Universität Karlsruhe, September 1994. (see also the CiteSeer page)
• D.J. Hand, Construction and Assessment of Classification Rules, Wiley, 1997.
• Michie, D., Spiegelhalter, D.J., and Taylor, C.C., Machine Learning, Neural and Statistical Classification, Ellis Horwood, New York, 1994.
• R.P.W. Duin, A note on comparing classifiers, Pattern Recognition Letters, vol. 17, no. 5, 1996, 529-536.
• D.A. Rachkovskij and E.M. Kussul, Datagen - a generator of datasets for evaluation of classification algorithms, Pattern Recognition Letters, vol. 19, no. 7, 1998, 537-544.

• Daniel Keysers uses the USPS dataset in a number of his papers.

• Paul C. Smits report experiments with a multispectral data set  published as the electronic annexes of:
 
Giorgio Giacinto, Fabio Roli and Lorenzo Bruzzone, "Combination of neural and statistical algorithms for supervised classification of remote-sensing images", Pattern Recognition Letters, 21 (5) (2000) pp. 385-397.
 
as well as  with hyperspectral remote sensing data which can be downloaded from the MultiSpec website maintained by Purdue University in:
 
P.C. Smits, "Combining Supervised Remote Sensing Image Classifiers Based on Individual Class Performances," In: Proceedings of the International workshop on Multiple Classifier Systems (MCS 01), Cambridge, UK, July 2-4, 2001. Lecture notes in Computer Science Vol. 2096: Springer, 2001.
 
• Robert P.W. Duin uses a multi-feature digit dataset in:
R.P.W. Duin and D.M.J. Tax, Experiments with Classifier Combining Rules, in: J. Kittler, F. Roli (eds.), Multiple Classifier Systems (Proc. First International Workshop, MCS 2000, Cagliari, Italy, June 2000), Lecture Notes in Computer Science, vol. 1857, Springer, Berlin, 2000, 16-29.

A.K. Jain, R.P.W. Duin, and J. Mao, Statistical Pattern Recognition: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, 2000, 4-37.

• Emilio Benfenati reports the data used in EC project COMET containing chemicals, descriptors (inputs) and toxicity values (output).

  data, 263kB (zipped Excel file), description, 25kB (Word file)

Workshops, conferences and upcoming events

Currently, IAPR-TC1 is involved in the organization, planning and promotion of several workshops. Past workshops with IAPR-TC1 involvement were:

• 1997 IAPR Workshop on Statistical Techniques in Pattern Recognition , Prague, CZ, June 9-11 1997.
• International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, Venice, Italy, May 21-23
• SPR'98 and SSPR'98, Joint international workshops on Syntactical and Structural Pattern Recognition & Statistical Pattern Recognition, Sydney, Australia, August 11-13, 1998.
• MCS 2000, First International Workshop on Multiple Classifier Systems, Cagliari, Italy, June 21-23, 2000.
• SPR 2000, Joint international workshops on Syntactical and Structural Pattern Recognition & Statistical Pattern Recognition, Alicante, Spain, August 30 - September 1, 2000.
• MCS 2001, Second International Workshop on Multiple Classifier Systems, Cambridge, UK, July 2-4, 2001.
• MCS 2002, Third International Workshop on Multiple Classifier Systems, Cagliari, Italy, June 24-26, 2002.
• SPR 2002, Joint international workshops on Syntactical and Structural Pattern Recognition & Statistical Pattern Recognition, Windsor, Canada, August 6-9, 2002.

• International Workshop on Multiple Classifier Systems MCS 2003, Guildford, UK, June 11-13, 2003.
• Workshops on Syntactical and Structural Pattern Recognition (SSPR 2004) and Statistical Pattern Recognition (SPR 2004), Lisbon, Portugal, August 18-20, 2004.