| Workshop Program |
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| 17.00 - 19.00 | Registration |
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| 08.00 - 08.45 | Registration |
| 08.45 - 09.00 | Opening |
| 09.00 - 10.00 | Invited Talk Ensemble Methods in Machine Learning T.G. Dietterich (Oregon State University, USA) |
| 10.00 - 10.20 | Coffee Break |
| 10.20 - 11.50 |
Session 1: Theoretical Issues A Mathematically Rigorous Foundation for Supervised Learning Complexity of Classification Problems and Comparative Advantages
of Combined Classifiers Priorities for future research on theoretical foundations
of multiple classifier systems |
| 11.50 -12.50 | Invited Talk Experiments with Classifier Combining Rules R.P.W. Duin, D.M.J. Tax (Delft University of Technology, The Netherlands) |
| 12.50 - 13.05 | Aims and Organisation of
the Final Round Table F. Roli and J. Kittler |
| 13.05 - 14.30 | Lunch |
| 14.30 - 15.30 | Invited Talk The "Test and Select" Approach to Ensemble Combination A.J.C. Sharkey, N.E. Sharkey, U. Gerecke, G.O. Chandroth (University of Sheffield, UK) |
| 15.30 - 15.50 | Coffee Break |
| 15.50 - 17.30 |
Session 2: Multiple Classifier Fusion Combining Fisher Linear Discriminants for Dissimilarity Representation A Learning Method of Feature Selection for Rough Classification Analysis of a Fusion Method for Combining Marginal Classifiers |
| 18.00 | Welcome Cocktail |
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| 08.30 - 10.10 |
Session 2: Multiple Classifier Fusion Combining Multiple Classifiers in Probabilistic Neural Networks
Supervised Classifier Combination Through Generalized Additive
Multi-model Dynamic Classifier Selection |
| 10.10 - 10.30 | Coffee Break |
| 10.30 - 11.30 | Invited Talk A Survey of Sequential Combination of Word Recognizers in Handwritten Phrase Recognition at CEDAR V. Govindaraju, S. Srihari (State University of New York at Buffalo, USA) |
| 11.30 - 13.10 |
Session 3: Bagging and Boosting Different Ways of Weakening Decision Trees and Their Impact
on Classification Accuracy of DT Combination Applying Boosting to Similarity Literals for Time Series Classification Boosting of Tree-Based Classifiers for Predicitve Risk Modeling
in GIS |
| 13.10 - 14.30 | Lunch |
| 14.30 - 15.30 | Invited Talk Multiple Classifier Combination Methodologies for Different Output Levels C.Y. Suen*, L. Lam*,** (*Concordia University, Canada; **Hong Kong Institute of Education, Hong Kong) |
| 15.30 - 16.15 | Coffee Break + Working Groups Time |
| 16.15 - 17.55 |
Session 4: Design of Multiple Classifier Systems Diversity Between Neural Networks and Decision Trees for Building
Multiple Classifier Systems Self-Organizing Decomposition of Functions in the Context
of a Unified Framework for Multiple Classifier Systems Classifier Instability and Partitioning |
| 20.30 | Social Dinner |
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| 08.30 - 10.10 |
Session 5: Remote-Sensing Data Analysis Consensus Based Classification of Multisource Remote Sensing
Data Combining Parametric and Nonparametric Classifiers for an
Unsupervised Updating of Land-Cover Maps A Multiple Self-Organizing Map Scheme for Remote Sensing Classification |
| 10.10 - 10.30 | Coffee Break |
| 10.30 - 12.10 |
Session 6: Document Analysis A Multi-expert System for Dynamic Signature Verification A Cascaded Multiple Expert System for Verification Architecture for Classifier Combination Using Entropy Measures |
| 12.10 - 13.10 | Invited Talk The Information Society Technologies Research Programme P.Corsi (European Commission, Directorate General Information Society) |
| 13.10 - 14.15 | Lunch |
| 14.15 - 16.20 |
Session 7: Miscellaneous Applications Statistical Sensor Calibration for Fusion of Different Classifiers
in a Biometric Person Recognition Framework A Modular Neuro-fuzzy Network for Musical Instruments Classification Classifier Combination for Grammar-Guided Sentence Recognition Shape Matching and Extraction by an Array of Figure-and-Ground
Classifiers |
| 16.20 - 16.40 | Coffee Break |
| 16.40 - 17.30 | Round Table: Priorities
for Future Research Chairmen: T.K. Ho, J. Kittler and F. Roli |
| 17.30 | Closing |