Regularized multi--task learning

TLDR

Learning multiple related tasks simultaneously can improve predictive performance compared to learning them independently, as shown by prior empirical studies. This paper proposes a multi‑task learning approach that minimizes regularization functionals analogous to those used in Support Vector Machines for single‑task learning. The method models task relationships with a novel kernel incorporating a task‑coupling parameter, is implemented in an SVM‑style framework, and is evaluated on simulated and real datasets. Experiments demonstrate that the proposed approach outperforms existing multi‑task learning methods and largely surpasses single‑task SVM performance.

Abstract

Past empirical work has shown that learning multiple related tasks from data simultaneously can be advantageous in terms of predictive performance relative to learning these tasks independently. In this paper we present an approach to multi--task learning based on the minimization of regularization functionals similar to existing ones, such as the one for Support Vector Machines (SVMs), that have been successfully used in the past for single--task learning. Our approach allows to model the relation between tasks in terms of a novel kernel function that uses a task--coupling parameter. We implement an instance of the proposed approach similar to SVMs and test it empirically using simulated as well as real data. The experimental results show that the proposed method performs better than existing multi--task learning methods and largely outperforms single--task learning using SVMs.