The handbook of brain theory and neural networks
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
On the Generalization Ability of GRLVQ Networks
Neural Processing Letters
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Support Vector Ordinal Regression
Neural Computation
Evaluation Measures for Ordinal Regression
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Adaptive relevance matrices in learning vector quantization
Neural Computation
Ordinal classification with decision rules
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Kernel Discriminant Learning for Ordinal Regression
IEEE Transactions on Knowledge and Data Engineering
Exploitation of pairwise class distances for ordinal classification
Neural Computation
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Neural Computation
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Many pattern analysis problems require classification of examples into naturally ordered classes. In such cases, nominal classification schemes will ignore the class order relationships, which can have a detrimental effect on classification accuracy. This article introduces two novel ordinal learning vector quantization (LVQ) schemes, with metric learning, specifically designed for classifying data items into ordered classes. In ordinal LVQ, unlike in nominal LVQ, the class order information is used during training in selecting the class prototypes to be adapted, as well as in determining the exact manner in which the prototypes get updated. Prototype-based models in general are more amenable to interpretations and can often be constructed at a smaller computational cost than alternative nonlinear classification models. Experiments demonstrate that the proposed ordinal LVQ formulations compare favorably with their nominal counterparts. Moreover, our methods achieve competitive performance against existing benchmark ordinal regression models.