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This paper investigates the efficacy of a regularized multi-task learning(MTL) framework based on SVM (M-SVM) to answer whether MTL always providesreliable results and how MTL outperforms independent learning. We first findthat M-SVM is Bayes risk consistent in the limit of large sample size. Thisimplies that despite the task dissimilarities, M-SVM always produces a reliabledecision rule for each task in terms of misclassification error when the datasize is large enough. Furthermore, we find that the task-interaction vanishesas the data size goes to infinity, and the convergence rates of M-SVM and itssingle-task counterpart have the same upper bound. The former suggests thatM-SVM cannot improve the limit classifier's performance; based on the latter,we conjecture that the optimal convergence rate is not improved when the tasknumber is fixed. As a novel insight of MTL, our theoretical and experimentalresults achieved an excellent agreement that the benefit of the MTL methodslies in the improvement of the pre-convergence-rate factor (PCR, to be denotedin Section III) rather than the convergence rate. Moreover, this improvement ofPCR factors is more significant when the data size is small.

Efficacy of regularized multi-task learning based on SVM models