Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Support Vector Machine Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Incremental Learning with Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Accurate on-line support vector regression
Neural Computation
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Incremental Support Vector Learning: Analysis, Implementation and Applications
The Journal of Machine Learning Research
Online prediction model based on support vector machine
Neurocomputing
Convergence improvement of active set training for support vector regressors
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
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Incremental and decremental processes of training a support vector machine (SVM) resumes to the migration of vectors in and out of the support set along with modifying the associated thresholds. This paper gives an overview of all the boundary conditions implied by vector migration through the incremental / decremental process. The analysis will show that the same procedures, with very slight variations, can be used for both the incremental and decremental learning. The case of vectors with duplicate contribution is also considered. Migration of vectors among sets on decreasing the regularization parameter is given particularly attention. Experimental data show the possibility of modifying this parameter on a large scale, varying it from complete training (overfitting) to a calibrated value.