Pairwise classification and support vector machines
Advances in kernel methods
A parallel mixture of SVMs for very large scale problems
Neural Computation
A parallel solver for large quadratic programs in training support vector machines
Parallel Computing - Special issue: Parallel computing in numerical optimization
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
An SVM classification algorithm with error correction ability applied to face recognition
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Support vector machines and the multiple hypothesis test problem
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image
IEEE Transactions on Image Processing
Parallel sequential minimal optimization for the training of support vector machines
IEEE Transactions on Neural Networks
Coded output support vector machine
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
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The Error Correction SVM method is an excellent multiclass classification approach and has been applied to face recognition successfully. Yet, it suffers from the computational complexity. To reduce the computation time of the algorithm, a parallel implementation scheme is presented in the paper in which the training and classification tasks are assigned to multiple processors and run on all the processors simultaneously. The simulation experiments conducted on a local area network using Cambridge ORL face database show that the parallel algorithm given in the paper is effective in speeding up the algorithms of the training and classification while maintaining the recognition accuracy unchanged.