Neural network design
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Using Discriminant Analysis for Multi-class Classification
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Multiclass Classification with Multi-Prototype Support Vector Machines
The Journal of Machine Learning Research
Computational identi?cation and characterization of Type III secretion substrates
CSBW '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops
Stochastic Organization of Output Codes in Multiclass Learning Problems
Neural Computation
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Applied Computational Intelligence and Soft Computing
Applied Computational Intelligence and Soft Computing
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In this paper, the multiclass supervised training problem is considered when a discrete set of classes is assumed. Upon generating affine models for finite data sets, we have observed the invariance of certain measures of performance after a trained classifier has been presented with test data of unknown classification. Specifically, after constructing mappings between training vectors and their desired targets, the class membership and ranking of test data has been found to remain either invariant or close to invariant under a transformation of the set of target vectors. Therefore, we derive conditions explaining how this type of invariance can arise when the multiclass problem is phrased in the context of linear networks. A bioinformatics example is then presented in order to demonstrate various principles outlined in this work.