Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Fast SVM Training Algorithm with Decomposition on Very Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental training of support vector machines
IEEE Transactions on Neural Networks
Artificial Intelligence in Medicine
The unimodal model for the classification of ordinal data
Neural Networks
Separating hypersurfaces of SVMs in input spaces
Pattern Recognition Letters
Ordinal-class core vector machine
Journal of Computer Science and Technology
Prototype based modelling for ordinal classification
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Adaptive metric learning vector quantization for ordinal classification
Neural Computation
Evolutionary extreme learning machine for ordinal regression
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Exploitation of pairwise class distances for ordinal classification
Neural Computation
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The cosmetic result is an important endpoint for breast cancer conservative treatment (BCCT), but the verification of this outcome remains without a standard. Objective assessment methods are preferred to overcome the drawbacks of subjective evaluation. In this paper a novel algorithm is proposed, based on support vector machines, for the classification of ordinal categorical data. This classifier is then applied as a new methodology for the objective assessment of the aesthetic result of BCCT. Based on the new classifier, a semi-objective score for quantification of the aesthetic results of BCCT was developed, allowing the discrimination of patients into four classes.