In this paper, we present a nonparametric Bayesian multi-task large-margin classification model which can cluster tasks into the most appropriate number of groups and induce flexible model sharing within each task group simultaneously. Specifically, we first show a very simple method to integrate large margin learning with hierarchical Bayesian models by employing an important variant of the standard SVM, i.e., proximal SVM (PSVM), whose loss function is used to define a novel likelihood function. And then we assume that the model parameter of each task consists of two parts: one is shared within each task group (group-level parameter) while the other is specific to each distinct task (task rescaling parameter). A Dirichlet process prior is imposed on the group-level parameter while the task rescaling parameter is assigned a one-mean Laplace prior. Finally the parameter of a task is the corresponding group parameter times its specific rescaling parameter. We give efficient Markov chain Monte Calo (MCMC) algorithm to conduct model inference. Experiments on the Landmine detection data and the UCI Yeast data demonstrate the effectiveness of our method.
['Changying Du', 'J. He', 'Fuzhen Zhuang', 'Yuan Qi', 'Qing He']
Nonparametric Bayesian multi-task large-margin classification