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MCS
2000
Springer
10 years 3 months ago
Combining Fisher Linear Discriminants for Dissimilarity Representations
Abstract Investigating a data set of the critical size makes a classification task difficult. Studying dissimilarity data refers to such a problem, since the number of samples equa...
Elzbieta Pekalska, Marina Skurichina, Robert P. W....
MCS
2000
Springer
10 years 3 months ago
Classifier Instability and Partitioning
Various methods exist for reducing correlation between classifiers in a multiple classifier framework. The expectation is that the composite classifier will exhibit improved perfor...
Terry Windeatt
MCS
2000
Springer
10 years 3 months ago
A Multi-expert System for Dynamic Signature Verification
This paper presents a multi-expert system for dynamic signature verification. The system combines three experts whose complementar behaviour is achieved by using both different fea...
Vincenzo Di Lecce, Giovanni Dimauro, Andrea Guerri...
MCS
2000
Springer
10 years 3 months ago
Experiments with Classifier Combining Rules
Abstract. A large experiment on combining classifiers is reported and discussed. It includes, both, the combination of different classifiers on the same feature set and the combina...
Robert P. W. Duin, David M. J. Tax
MCS
2000
Springer
10 years 3 months ago
Ensemble Methods in Machine Learning
Ensemble methods are learning algorithms that construct a set of classi ers and then classify new data points by taking a (weighted) vote of their predictions. The original ensembl...
Thomas G. Dietterich
MCS
2000
Springer
10 years 3 months ago
A Hybrid Projection Based and Radial Basis Function Architecture
We introduce a mechanism for constructing and training a hybrid architecture of projection based units and radial basis functions. In particular, we introduce an optimization sche...
Shimon Cohen, Nathan Intrator
MCS
2000
Springer
10 years 3 months ago
Analysis of a Fusion Method for Combining Marginal Classifiers
The use of multiple features by a classifier often leads to a reduced probability of error, but the design of an optimal Bayesian classifier for multiple features is dependent on t...
Mark D. Happel, Peter Bock
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