Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data
KDD, pp. 2003-2011, 2018.
Kernel methods are powerful supervised machine learning models for their strong generalization ability, especially on limited data to effectively generalize on unseen data. However, most kernel methods, including the state-of-the-art LIBSVM, are vulnerable to the curse of kernelization, making them infeasible to apply to large-scale datas...More
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