Multiclass discriminant mappings
Signal Processing
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
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Generalized Representer Theorem
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Methods for alignment of multi-class signal sets
Signal Processing
Everything old is new again: a fresh look at historical approaches in machine learning
Everything old is new again: a fresh look at historical approaches in machine learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
The Journal of Machine Learning Research
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
On Learning Vector-Valued Functions
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A novel and quick SVM-based multi-class classifier
Pattern Recognition
Multi-class pattern classification using neural networks
Pattern Recognition
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Leave-one-out support vector machines
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Nonconvex Online Support Vector Machines
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information---Theoretic Multiclass Classification Based on Binary Classifiers
Journal of Signal Processing Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
Information Sciences: an International Journal
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In this paper, we study the multiclass classification problem. We derive a framework to solve this problem by providing algorithms with the complexity of a single binary classifier. The resulting multiclass machines can be decomposed into two categories. The first category corresponds to vector-output machines, where we develop several algorithms. In the second category, we show that the least-squares classifier can be easily cast into a multiclass one-versus-all scheme, without the need to train multiple binary classifiers. The proposed framework shows that, while keeping the classification accuracy essentially unchanged, the computational complexity is orders of magnitude lower than those previously reported in the literature. This makes our approach extremely powerful and conceptually simple. Moreover, we study the coding of the multiclass labels, and demonstrate that several celebrated approaches are equivalent. These arguments are illustrated with experimentations on well-known benchmarks.