Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Locality preserving CCA with applications to data visualization and pose estimation
Image and Vision Computing
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
An incremental node embedding technique for error correcting output codes
Pattern Recognition
Subclass Problem-Dependent Design for Error-Correcting Output Codes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Error-Correcting Ouput Codes Library
The Journal of Machine Learning Research
Structural Regularized Support Vector Machine: A Framework for Structural Large Margin Classifier
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
Error correcting output codes (ECOC) is a popular framework for addressing multi-class classification problems by combing multiple binary sub-problems. In each binary sub-problem, at least one class is actually a ''meta-class'' consisting of multiple original classes, and treated as a single class in the learning process. This strategy brings a simple and common implementation of multi-class classification, but simultaneously, results in the under-exploitation of already-provided structure knowledge in individual original classes. In this paper, we present a new methodology to show that the utilization of such prior structure knowledge can further strengthen the performance of ECOCs, and the structure knowledge is formulated under the cluster and manifold assumptions, respectively. Finally, we validate our methodology on both toy and real benchmark datasets (UCI, face recognition and objective category), consequently validate the structure knowledge of individual original classes for ECOC-based multi-class classification.