Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Convex Optimization
Approximate minimum enclosing balls in high dimensions using core-sets
Journal of Experimental Algorithmics (JEA)
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
New approaches to support vector ordinal regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Core Vector Regression for very large regression problems
ICML '05 Proceedings of the 22nd international conference on Machine learning
Neural Networks - 2005 Special issue: IJCNN 2005
Minimum Enclosing Spheres Formulations for Support Vector Ordinal Regression
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Multiclass core vector machine
Proceedings of the 24th international conference on Machine learning
Simpler core vector machines with enclosing balls
Proceedings of the 24th international conference on Machine learning
Learning to Classify Ordinal Data: The Data Replication Method
The Journal of Machine Learning Research
Computational Geometry: Theory and Applications
Generalized Core Vector Machines
IEEE Transactions on Neural Networks
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Ordinal regression is one of the most important tasks of relation learning, and several techniques based on support vector machines (SVMs) have also been proposed for tackling it, but the scalability aspect of these approaches to handle large datasets still needs much of exploration. In this paper, we will extend the recent proposed algorithm Core Vector Machine (CVM) to the ordinal-class data, and propose a new algorithm named as Ordinal-Class Core Vector Machine (OCVM). Similar with CVM, its asymptotic time complexity is linear with the number of training samples, while the space complexity is independent with the number of training samples. We also give some analysis for OCVM, which mainly includes two parts, the first one shows that OCVM can guarantee that the biases are unique and properly ordered under some situation; the second one illustrates the approximate convergence of the solution from the viewpoints of objective function and KKT conditions. Experiments on several synthetic and real world datasets demonstrate that OCVM scales well with the size of the dataset and can achieve comparable generalization performance with existing SVM implementations.