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
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Efficient SVM Regression Training with SMO
Machine Learning
Convergence of a Generalized SMO Algorithm for SVM Classifier Design
Machine Learning
Training Invariant Support Vector Machines
Machine Learning
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)
Handwritten Numeral Recognition Using Gradient and Curvature of Gray Scale Image
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
SVM in oracle database 10g: removing the barriers to widespread adoption of support vector machines
VLDB '05 Proceedings of the 31st international conference on Very large data bases
An improved handwritten Chinese character recognition system using support vector machine
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Data-driven decomposition for multi-class classification
Pattern Recognition
Data preparation for sample-based face detection
International Journal of Computer Applications in Technology
A fast parallel optimization for training support vector machine
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
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A fast support vector machine (SVM) training algorithm is proposed under the decomposition framework of SVM's algorithm by effectively integrating kernel caching, digest and shrinking policies and stopping conditions. Extensive experiments on MNIST handwritten digit database have been conducted to show that the proposed algorithm is much faster than Keerthi et al.'s improved SMO, about 9 times. Combined with principal component analysis, the total training for ten one against the-rest classifiers on MNIST took just 0.77 hours. The promising scalability of the proposed scheme can make it possible to apply SVM to a wide variety of problems in engineering.