Characterization of the Sonar Signals Benchmark
Neural Processing Letters
Making large-scale support vector machine learning practical
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
Machine Learning
Fuzzy Rule Extraction from Support Vector Machines
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Support vector learning for fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
A digital architecture for support vector machines: theory, algorithm, and FPGA implementation
IEEE Transactions on Neural Networks
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The implementation of training algorithms for SVMs on embedded architectures differs significantly from the electronic support of trained SVM systems. This mostly depends on the complexity and the computational intricacies brought about by the optimization process, which implies a Quadratic-Programming problem and usually involves large data sets. This work presents a general approach to the efficient implementation of SVM training on Digital Signal Processor (DSP) devices. The methodology optimizes efficiency by suitably adjusting the established, effective Keerthi's optimization algorithm for large data sets. Besides, the algorithm is reformulated to best exploit the computational features of DSP devices and boost efficiency accordingly. Experimental results tackle the training problem of SVMs by involving real-world benchmarks, and confirm both the computational efficiency of the approach.