The nature of statistical learning theory
The nature of statistical learning theory
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Multiclass LS-SVMs: Moderated Outputs and Coding-Decoding Schemes
Neural Processing Letters
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Experiments with Random Projection
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Minimization of SC1 functions and the Maratos effect
Operations Research Letters
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
A Multi-criteria Convex Quadratic Programming model for credit data analysis
Decision Support Systems
Extracting shared subspace for multi-label classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Nonlinear Proximal Support Vector Machine
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Computers in Biology and Medicine
Information Sciences: an International Journal
A shared-subspace learning framework for multi-label classification
ACM Transactions on Knowledge Discovery from Data (TKDD)
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Fuzzy hyper-prototype clustering
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Expert Systems with Applications: An International Journal
A simplified multi-class support vector machine with reduced dual optimization
Pattern Recognition Letters
Modeling and optimization of high-technology manufacturing productivity
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
A new multi-criteria convex quadratic programming model for credit analysis
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
A spatially constrained fuzzy hyper-prototype clustering algorithm
Pattern Recognition
A novel chinese text feature selection method based on probability latent semantic analysis
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
A weighted twin support vector regression
Knowledge-Based Systems
Inverse matrix-free incremental proximal support vector machine
Decision Support Systems
Review: Supervised classification and mathematical optimization
Computers and Operations Research
A proximal classifier with consistency
Knowledge-Based Systems
Efficient sparse least squares support vector machines for pattern classification
Computers & Mathematics with Applications
FHC: The fuzzy hyper-prototype clustering algorithm
International Journal of Knowledge-based and Intelligent Engineering Systems - Intelligent Information Processing: Techniques and Applications
Extending twin support vector machine classifier for multi-category classification problems
Intelligent Data Analysis
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Given a dataset, each element of which labeled by one of k labels, we construct by a very fast algorithm, a k-category proximal support vector machine (PSVM) classifier. Proximal support vector machines and related approaches (Fung & Mangasarian, 2001; Suykens & Vandewalle, 1999) can be interpreted as ridge regression applied to classification problems (Evgeniou, Pontil, & Poggio, 2000). Extensive computational results have shown the effectiveness of PSVM for two-class classification problems where the separating plane is constructed in time that can be as little as two orders of magnitude shorter than that of conventional support vector machines. When PSVM is applied to problems with more than two classes, the well known one-from-the-rest approach is a natural choice in order to take advantage of its fast performance. However, there is a drawback associated with this one-from-the-rest approach. The resulting two-class problems are often very unbalanced, leading in some cases to poor performance. We propose balancing the k classes and a novel Newton refinement modification to PSVM in order to deal with this problem. Computational results indicate that these two modifications preserve the speed of PSVM while often leading to significant test set improvement over a plain PSVM one-from-the-rest application. The modified approach is considerably faster than other one-from-the-rest methods that use conventional SVM formulations, while still giving comparable test set correctness.