A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Self-organizing maps
Pairwise classification and support vector machines
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
On the Use of Self-Organizing Maps for Clustering and Visualization
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Analysis and visualization of gene expression data using self-organizing maps
Neural Networks - New developments in self-organizing maps
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
A hierarchical method for multi-class support vector machines
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Margin Trees for High-dimensional Classification
The Journal of Machine Learning Research
Half-Against-Half multi-class support vector machines
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Learning data structure from classes: A case study applied to population genetics
Information Sciences: an International Journal
Enhancing directed binary trees for multi-class classification
Information Sciences: an International Journal
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From a multi-class learning task, in addition to a classifier, it is possible to infer some useful knowledge about the relationship between the classes involved. In this paper we propose a method to learn a hierarchical clustering of the set of classes. The usefulness of such clusterings has been exploited in bio-medical applications to find out relations between diseases or populations of animals. The method proposed here defines a distance between classes based on the margin maximization principle, and then builds the hierarchy using a linkage procedure. Moreover, to quantify the goodness of the hierarchies we define a measure. Finally, we present a set of experiments comparing the scores achieved by our approach with other methods.