The nature of statistical learning theory
The nature of statistical learning theory
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
A parallel mixture of SVMs for very large scale problems
Neural Computation
Efficient svm training using low-rank kernel representations
The Journal of Machine Learning Research
Accurate on-line support vector regression
Neural Computation
Fast SVM Training Algorithm with Decomposition on Very Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs
The Journal of Machine Learning Research
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
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
Training a Support Vector Machine in the Primal
Neural Computation
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
Incremental Support Vector Learning: Analysis, Implementation and Applications
The Journal of Machine Learning Research
A fast iterative nearest point algorithm for support vector machine classifier design
IEEE Transactions on Neural Networks
A study on reduced support vector machines
IEEE Transactions on Neural Networks
Incremental training of support vector machines
IEEE Transactions on Neural Networks
Realtime training on mobile devices for face recognition applications
Pattern Recognition
Incremental face recognition for large-scale social network services
Pattern Recognition
An incremental approach to support vector machine learning
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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Most algorithms of support vector machines (SVMs) operate in a batch mode. However, when the samples arrive sequentially, batch implementations of SVMs are computationally demanding due to the fact that they must be retrained from scratch. This paper proposes an incremental SVM algorithm that is suitable for the problems of sequentially arriving samples. Unlike previous SVM techniques, this new incremental SVM learning is implemented in the primal and it shows that the primal problem can be efficiently solved. The effectiveness of the proposed method is illustrated with several data sets including faces, handwritten characters and UCI data sets. These experiments also show that the proposed method is competitive with previously published methods. In addition, the application of the proposed algorithm to leave-one-out cross-validation is demonstrated.