Presentations Robert P.W. Duin

Selected Papers and Presentations of Robert P.W. Duin

On this page selected papers and the sheets of some presentations are made available.
See also my home page for accessing all publications.

Bob Duin

Pattern Recognition in general

Representation

Compactness and dataset complexity

Classifier evaluation

Classifier combining

Small sample size classification

Neural networks

Pattern recognition for spectral imaging

Pattern Recognition in general

Sheets

Ups and downs in pattern recognition, Retirement talk, 28 October, 2011.
Pattern Recognition, steps in science and consciousness, Tutorial AERFAI Summer School on New Trends in Pattern Recognition for Motion Analysis, Computer Vision Center, Bellaterra, Barcelona , 7th-11th July, 2008.

Related papers

R.P.W. Duin, The Origin of Patterns, Frontiers in Computer Science, 2021.
R.P.W. Duin and E. Pekalska, The Science of Pattern Recognition; Achievements and Perspectives, in: W. Duch, J. Mandziuk (eds.), Challenges for Computational Intelligence, Studies in Computational Intelligence, vol. 63, Springer, 2007, 221-259.

R.P.W. Duin and E. Pekalska, Structural inference of sensor-based measurements, in: D.-Y. Yeung, J.T. Kwok, A. Fred, F. Roli and D. de Ridder (eds.), Structural, Syntactic, and Statistical Pattern Recognition, Proc. SSSPR2006, Lecture Notes in Computer Science, vol. 4109, Springer Verlag, Berlin, 2006, 41-55.

e-Book

· R.P.W. Duin and E. Pekalska, Pattern Recognition: Introduction and Terminology, eBook, 37Steps, 2015, 1-78.

Representation

Sheets (Presentations are similar, but gradually developing)

Structural Pattern Recognition in Dissimilarity Space, FEAST 2015, Lille, 10 July 2015.
Non-Euclidean Problems in Pattern Recognition, ACIT2013, Khartoum 17-19 December 2013.
The Dissimilarity Representation for Non-Euclidean Pattern Recognition, Tutorial, 2011.
The Dissimilarity Representation for Structural Pattern Recognition, CIARP 2011, DICTA 2011.
Training classifiers for non- Euclidean data, Seminar Johns Hopkins University – AMS, Baltimore, 6 March 2008.
The Dissimilarity Representation for Pattern Recognition, Seminar Johns Hopkins University – CIS, Baltimore , 4 March 2008.
Structural Inference of Sensor-Based Measurements, Pierre Devijver Lecture, SSSPR, Hong Kong, 2006.
Prototype Selection for Dissimilarity-based Classifiers , Conferenza Italiana sui Sistemi Intelligenti, Perugia, Italy, 15-17 September 2004.
The dissimilarity representation, a basis for domain based pattern recognition?, Workshop on Pattern Representation and the Future of Pattern Recognition, Cambridge, UK, 22 August 2004.
Dissimilarity Representations for Pattern Recognition, Meeting Spanish Pattern Recognition Network, Madrid, September 4-5, 2003.

Tutorial

R.P.W. Duin and E. Pekalska, The Dissimilarity Representation for Non-Euclidean Pattern Recognition, 2011

Related book

E. Pekalska and R.P.W. Duin, The Dissimilarity Representation for Pattern Recognition, Foundations and Applications, World Scientific, Singapore, 2005, 607 pages, ISBN 981-256-530-2.

Related papers

R.P.W. Duin, E. Pekalska, and M. Loog, Non-Euclidean Dissimilarities: Causes, Embedding and Informativeness, in: M. Pelillo (eds.), Similarity-Based Pattern Analysis and Recognition, Advances in Computer Vision and Pattern Recognition, Springer, 2013, 13-44.
W.R. Lee, V. Cheplygina, D.M.J. Tax, M. Loog, and R.P.W. Duin, Bridging Structure and Feature Representations in Graph Matching, International Journal of Pattern Recognition and Artificial Intelligence, vol. 26, no. 5, 2012.
R.P.W. Duin and E. Pekalska, The dissimilarity space: Bridging structural and statistical pattern recognition, Pattern Recognition Letters, vol. 33, no. 7, 2012, 826-832.
R.P.W. Duin, Non- Euclidean Problems in Pattern Recognition Related to Human Expert Knowledge, in: J. Filipe, J. Cordeiro (eds.), Enterprise Information Systems, Revised Selected Papers ICEIS 2010, Lecture Notes in Business Information Processing, vol. 73, Springer, 2011, 15-28.
E. Pekalska and R.P.W. Duin, Beyond traditional kernels: classification in two dissimilarity-based representation spaces, IEEE Transactions on Systems, Man Cybernetics, vol. 38, no. 6, 2008, 729-744.
R.P.W. Duin and E. Pekalska, Structural inference of sensor-based measurements, in: D.-Y. Yeung, J.T. Kwok, A. Fred, F. Roli and D. de Ridder (eds.), Structural, Syntactic, and Statistical Pattern Recognition, Proc. SSSPR2006, Lecture Notes in Computer Science, vol. 4109, Springer Verlag, Berlin, 2006, 41-55.
E. Pekalska, R.P.W. Duin, and P. Paclik, Prototype selection for dissimilarity-based classification, Pattern Recognition, vol. 39, no. 2, 2006, 189-208.
R.P.W. Duin, E. Pekalska, P. Paclik, and D.M.J. Tax, The dissimilarity representation, a basis for domain based pattern recognition?, in: L. Goldfarb (eds.), Pattern representation and the future of pattern recognition, (ICPR 2004 Workshop Proceedings, Cambridge UK, 22 August 2004), Faculty of Computer Science, Univ. of New Brunswick, Fredericton, NB, Canada, 2004, 43-56.
E. Pekalska, P. Paclik, and R.P.W. Duin, A Generalized Kernel Approach to Dissimilarity-based Classification, Journal of Machine Learning Research, Special Issue on Kernel Methods, vol. 2, no. 2, 2002, 175-211.
E. Pekalska and R.P.W. Duin, Dissimilarity representations allow for building good classifiers, Pattern Recognition Letters, vol. 23, no. 8, 2002, 943-956.
E. Pekalska and R.P.W. Duin, Automatic pattern recognition by similarity representations, Electronics Letters, vol. 37, no. 3, 2001, 159-160.
R.P.W. Duin, E. Pekalska, and D. de Ridder, Relational discriminant analysis, Pattern Recognition Letters, vol. 20, no. 11-13, 1999, 1175-1181.
R.P.W. Duin, D. de Ridder, and D.M.J. Tax, Experiments with object based discriminant functions; a featureless approach to pattern recognition, Pattern Recognition Letters, vol. 18, no. 11-13, 1997, 1159-1166.

Compactness and dataset complexity

Sheets

The characterization of classification problems by classifier disagreements, ICPR2004, Cambridge , 22-26 August 2004.

Related papers

R.P.W. Duin and E. Pekalska, Object representation, sample size and data complexity, in: M. Basu and T.K. Ho (eds.), Data Complexity in Pattern Recognition, Springer, London, 2006, 25-47.
R.P.W. Duin, E. Pekalska, and D.M.J. Tax, The characterization of classification problems by classifier disagreements, in: J. Kittler, M. Petrou, M. Nixon (eds.), Proc. 17th International Conference on Pattern Recognition (22-26 August 2004, Cambridge UK), vol. 2, IEEE Computer Society, Los Alamitos, CA, 2004, 140-143.
E. Pekalska, R.P.W. Duin, and M. Skurichina, A discussion on the classifier projection space for classifier combining, in: F. Roli, J. Kittler (eds.), Multiple Classifier Systems, Proceedings Third International Workshop MCS 2002 (Cagliari, Italy, June 24-26), Lecture Notes in Computer Science, vol. 2364, Springer Verlag, Berlin, 2002, 137-148.
R.P.W. Duin and E. Pekalska, Complexity of Dissimilarity based Pattern Classes, in: I. Austvoll (eds.), Proc. of the 12th Scandinavian Conference on Image Analysis, SCIA 2001 (Bergen, Norway, June 11-14), NOBIM, Stavanger, Norway, 2001, 663-670.
R.P.W. Duin, Compactness and Complexity of Pattern Recognition Problems, in: C. Perneel (eds.), Proc. Int. Symposium on Pattern Recognition "In Memoriam Pierre Devijver" ( Brussels , B, Feb.12), Royal Military Academy , Brussels , 1999, 124-128.

Classifier evaluation

Papers

R.P.W. Duin, M. Loog, E. Pekalska, and D.M.J. Tax, Feature-based Dissimilarity Space Classification, in: D. ay, Z.taltepe, and S. Aksoy (eds.), Recognizing Patterns in Signals, Speech, Images, and Videos, ICPR 2010, Lecture Notes in Computer Science, vol. 6388, Springer, 2010, 46-55. (This paper is based on a contribution to the ICPR 2010 classifier landscape constest)
R.P.W. Duin, A note on comparing classifiers, Pattern Recognition Letters, vol. 17, no. 5, 1996, 529-536.

Classifier combining

Sheets

Combining Feature Subsets in Feature Selection , MCS2005, Seaside, CA, USA, June 13-15, 2005.
The Combining Classifier: To Train Or Not To Train, ICPR2002, Quebec City, August 11-15, 2002.
Experiments with Classifier Combining Rules, MCS2000, Cagliari , June21-23, 2000.

Related paper

R.P.W. Duin, The Combining Classifier: To Train Or Not To Train?, in: R. Kasturi, D. Laurendeau, C. Suen (eds.), ICPR16, Proceedings 16th International Conference on Pattern Recognition (August 11-15, 2002, Quebec City, Canada), vol. II, IEEE Computer Society Press, Los Alamitos, 2002, 765-770.
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.

Small sample size classification

Sheets

Pattern Recognition in Almost Empty Spaces, WIC Winter Symposium, Eindhoven, 19 January 2004
Classifiers in Almost Empty Spaces, ICPR2000, Barcelona , 3-7 September 2000

Related paper

R.P.W. Duin, Classifiers in Almost Empty Spaces, in: A. Sanfeliu, J.J. Villanueva, M. Vanrell, R. Alquezar, A.K. Jain, J. Kittler (eds.), ICPR15, Proc. 15th Int. Conference on Pattern Recognition (Barcelona, Spain, Sep.3-7), vol. 2, Pattern Recognition and Neural Networks, IEEE Computer Society Press, Los Alamitos, 2000, 1-7.

Neural networks

Sheets

Learned from Neural Networks, ASCI2000, Lommel, Belgium, June 14-16, 2000

Related papers

R.P.W. Duin, Four Scientific Approaches to Pattern Recognition, in: A.M. Vossepoel, F.M. Vos (eds.), Fourth Quinquennial Review 1996-2001 Dutch Society for Pattern Recognition and Image Processing, NVPHBV, Delft, 2001, 331-337.
R.P.W. Duin, Learned from Neural Networks (Theme Presentation), in: L.J. van Vliet, J.W.J. Heijnsdijk, T. Kielman, P.M.W. Knijnenburg (eds.), Proc. ASCI 2000, 6th Annual Conf. of the Advanced School for Computing and Imaging (Lommel, Belgium, June 14-16), ASCI, Delft, 2000, 9-13.
R.P.W. Duin, Superlearning and neural network magic (IAPR discussion pages), Pattern Recognition Letters, vol. 15, no. 3, 1994, 215-217.
R.P.W. Duin, Superlearning capabilities of neural networks?, SCIA'93, Proc. of the 8th Scandinavian Conf. on Image Analysis (Tromso, Norway, May 25-28), NOBIM, Norwegian Society for Image Processing and Pattern Recognition, Tromso, Norway, 1993, 547-554.

Pattern recognition for spectral imaging

Sheets

Research challenges in spectral and spatial data analysis, 2^nd Spectral Imaging Workshop, Villach , 19-20 September 2005.
Pattern recognition for hyperspecral images, Chemical Imaging Across the Scales: From Microns to Miles, 22^th Annual Symposium on Chemometrics, Dutch Chemometrics Society, Nijmegen, 20 May 2005.
Pattern recognition for spectral imaging, Spectral Imaging Workshop, Graz, 3 April 2003.

Related papers

P. Paclik, R. Leitner, and R.P.W. Duin, A study on design of object sorting algorithms in the industrial application using hyperspectral imaging, Journal of Real-Time Image Processing, vol. 1, no. 2, 2006, 101-108.
P. Paclik and R.P.W. Duin, Designing multi-modal classifiers of spectra: a study on industrial sorting application, in: R. Leitner (eds.), Spectral Imaging (Proc. 2nd Int. Workshop of the Carinthian Tech Research AG, Villach, Austria, Sep 19-20,2005), Austrian Computer Society, 2005, 19-25.
P. Paclik, S. Verzakov, and R.P.W. Duin, Multi-class extensions of the GLDB feature extraction algorithm for spectral data, in: J. Kittler, M. Petrou, M. Nixon (eds.), Proc. 17th In.l Conf. on Pattern Recognition (22-26 August 2004, Cambridge UK), vol. 4, IEEE Computer Society, Los Alamitos, CA, 2004, 629-632.
S. Verzakov, P. Paclik, and R.P.W. Duin, Feature shaving for spectroscopic data, in: A. Fred, T. Caelli, R.P.W. Duin, A. Campilho, and D. de Ridder (eds.), Structural, Syntactic, and Statistical Pattern Recognition, Proc. SSSPR2004 (Lisbon, Portugal, August 2004), Lecture Notes in Computer Science, vol. 3138, Springer Verlag, Berlin, 2004, 1026-1033.
P. Paclik and R.P.W. Duin, Dissimilarity-based classification of spectra: computational issues, Real Time Imaging Journal, vol. 9, no. 4, 2003, 237-244.
P. Paclik, R.P.W. Duin, G.M.P. van Kempen, and R. Kohlus, Segmentation of multi-spectral images using the combined classifier approach, Image and Vision Computing Journal, vol. 21, no.6, 2003, 473-482.

Latest update: February 2007