FPGA Simulation of Linear and Nonlinear Support Vector Machine

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the kernel functions respond better classification results

than the others, if the solutions to implement all these

functions exist, the designer will have a tendency to

achieve the best classification accuracy.

Another suggestion is to implement the entire process

of SVM, including training and testing phase, on the

FPGA. Training phase of the SVM includes optimization

problem Equation (2), which has a very time consuming

and complicated process of solution for hardware im-

plementation purpose. If an appropriate and hardware

friendly solution is found to simplify the problem, then

whole process of SVM implemented on the FPGA, so

that no need to implement the training phase in software

environment which may cause quantization errors in

testing phase hardware implementation.

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