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
On-line learning and stochastic approximations
On-line learning in neural networks
Handling concept drifts in incremental learning with support vector machines
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Support Vector Data Description
Machine Learning
Face Recognition Using Laplacianfaces
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
An online support vector machine for abnormal events detection
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Face detection using discriminating feature analysis and Support Vector Machine
Pattern Recognition
Solving multiclass support vector machines with LaRank
Proceedings of the 24th international conference on Machine learning
Incremental support vector machine framework for visual sensor networks
EURASIP Journal on Applied Signal Processing
Large-Scale Kernel Machines (Neural Information Processing)
Large-Scale Kernel Machines (Neural Information Processing)
Computational Geometry: Theory and Applications
Model selection for the LS-SVM. Application to handwriting recognition
Pattern Recognition
On-line independent support vector machines
Pattern Recognition
IEEE Transactions on Signal Processing
A Low-Cost Pedestrian-Detection System With a Single Optical Camera
IEEE Transactions on Intelligent Transportation Systems
On the convergence of the decomposition method for support vector machines
IEEE Transactions on Neural Networks
Incremental training of support vector machines
IEEE Transactions on Neural Networks
Generalized Core Vector Machines
IEEE Transactions on Neural Networks
Two one-pass algorithms for data stream classification using approximate MEBs
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
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Support vector machine (SVM) is a widely used classification technique. However, it is difficult to use SVMs to deal with very large data sets efficiently. Although decomposed SVMs (DSVMs) and core vector machines (CVMs) have been proposed to overcome this difficulty, they cannot be applied to online classification (or classification with learning ability) because, when new coming samples are misclassified, the classifier has to be adjusted based on the new coming misclassified samples and all the training samples. The purpose of this paper is to address this issue by proposing an online CVM classifier with adaptive minimum-enclosing-ball (MEB) adjustment, called online CVMs (OCVMs). The OCVM algorithm has two features: (1) many training samples are permanently deleted during the training process, which would not influence the final trained classifier; (2) with a limited number of selected samples obtained in the training step, the adjustment of the classifier can be made online based on new coming misclassified samples. Experiments on both synthetic and real-world data have shown the validity and effectiveness of the OCVM algorithm.