Mudhar Bin Rabieah

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Support Vector Machines (SVMs) are powerful supervised learning methods in machine learning. However, their applicability to large problems has been limited due to the time consuming training stage whose computational cost scales quadratically with the number of examples. In this work, a complete FPGA-based system for nonlinear SVM training using ensemble(More)
Support Vector Machines (SVM) are powerful supervised learnings method in machine learning. However, their applicability to large problems, where frequent retraining of the system is required, has been limited due to the time consuming training stage whose computational cost scales quadratically with the number of examples. In this work, a complete(More)
Support Vector Machines (SVMs) are powerful supervised learning methods in machine learning. However, their applicability to large problems has been limited due to the time consuming training stage whose computational cost scales quadratically with the number of examples. In this work, a complete FPGA-based system for nonlinear SVM training using ensemble(More)
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