Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Classifier Conditional Posterior Probabilities
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
A New Approach of Modifying SVM Outputs
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Moderating the outputs of support vector machine classifiers
IEEE Transactions on Neural Networks
General solution and learning method for binary classification with performance constraints
Pattern Recognition Letters
Classification with a Reject Option using a Hinge Loss
The Journal of Machine Learning Research
A Hybrid Approach Handling Imbalanced Datasets
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
A ROC-based reject rule for support vector machines
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
On rejecting unreliably classified patterns
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
On the Foundations of Noise-free Selective Classification
The Journal of Machine Learning Research
A multi-objective optimisation approach for class imbalance learning
Pattern Recognition
Diagnostic of pathology on the vertebral column with embedded reject option
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
A family of measures for best top-n class-selective decision rules
Pattern Recognition
Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles
Machine Vision and Applications
Hybrid model of clustering and kernel autoassociator for reliable vehicle type classification
Machine Vision and Applications
The data replication method for the classification with reject option
AI Communications
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In this paper, the problem of implementing the reject option in support vector machines (SVMs) is addressed. We started by observing that methods proposed so far simply apply a reject threshold to the outputs of a trained SVM. We then showed that, under the framework of the structural risk minimisation principle, the rejection region must be determined during the training phase of a classifier. By applying this concept, and by following Vapnik's approach, we developed a maximum margin classifier with reject option. This led us to a SVM whose rejection region is determined during the training phase, that is, a SVM with embedded reject option. To implement such a SVM, we devised a novel formulation of the SVM training problem and developed a specific algorithm to solve it. Preliminary results on a character recognition problem show the advantages of the proposed SVM in terms of the achievable error-reject trade-off.