Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Warm start of the primal-dual method applied in the cutting-plane scheme
Mathematical Programming: Series A and B
Introduction to Algorithms
Warm-Start Strategies in Interior-Point Methods for Linear Programming
SIAM Journal on Optimization
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Reoptimization With the Primal-Dual Interior Point Method
SIAM Journal on Optimization
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Incremental training of support vector machines using hyperspheres
Pattern Recognition Letters
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Incremental Support Vector Learning: Analysis, Implementation and Applications
The Journal of Machine Learning Research
Multiplicative Updates for Nonnegative Quadratic Programming
Neural Computation
Solving multiclass support vector machines with LaRank
Proceedings of the 24th international conference on Machine learning
Computational Optimization and Applications
A Multi-class Incremental and Decremental SVM Approach Using Adaptive Directed Acyclic Graphs
ICAIS '09 Proceedings of the 2009 International Conference on Adaptive and Intelligent Systems
Incremental Training of Multiclass Support Vector Machines
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
The huller: a simple and efficient online SVM
ECML'05 Proceedings of the 16th European conference on Machine Learning
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Incremental training of support vector machines
IEEE Transactions on Neural Networks
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We present a new method for the incremental training of multiclass support vector machines that can simultaneously modify each class separating hyperplane and provide computational efficiency for training tasks where the training data collection is sequentially enriched and dynamic adaptation of the classifier is required over time. An auxiliary function has been designed, that incorporates some desired characteristics in order to provide an upper bound for the objective function, which summarizes the multiclass classification task. A novel set of multiplicative update rules is proposed, which is independent from any kind of learning rate parameter, provides computational efficiency compared to the conventional batch training approach and is easy to implement. Convergence to the global minimum is guaranteed, since the optimization problem is convex and the global minimizer for the enriched dataset is found using a warm-start algorithm. Experimental evidence on various data collections verified that our method is faster than retraining the classifier from scratch, while the achieved classification accuracy rate is maintained at the same level.