Fast Linear Optimisation with Automatically Biased Support Vector Machines
نویسنده
چکیده
We propose a new Support Vector Machine classifier formulation which allows for an automatic computation of the bias and eliminates the equality constraint. We also present a new training algorithm, which is capable of providing fast training for our automatically biased SVM. We then show that this method allows for the application of acceleration methods which further increases the rates of convergence. Comparisons between our agorithm to the well-known Sequential Minimal Optimization (SMO) algorithm are also made.
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