The handbook of brain theory and neural networks
Neural Networks - 2005 Special issue: IJCNN 2005
Support Vector Ordinal Regression
Neural Computation
MINLIP: Efficient Learning of Transformation Models
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Adaptive relevance matrices in learning vector quantization
Neural Computation
Kernel Discriminant Learning for Ordinal Regression
IEEE Transactions on Knowledge and Data Engineering
Hi-index | 0.00 |
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 detrimental effect on classification accuracy. This paper introduces a novel ordinal Learning Vector Quantization (LVQ) scheme, with metric learning, specifically designed for classifying data items into ordered classes. Unlike in nominal LVQ, in ordinal LVQ the class order information is utilized during training in selection of the class prototypes to be adapted, as well as in determining the exact manner in which the prototypes get updated. Prototype based models are in general more amenable to interpretations and can often be constructed at a smaller computational cost than alternative non-linear classification models. Experiments demonstrate that the proposed ordinal LVQ formulation compares favorably with its nominal counterpart. Moreover, our method achieves competitive performance against existing benchmark ordinal regression models.