Kernel online multi-task learning

Abstract

Many real world learning problems can be divided into a number of dependent subtasks. The conventional machine learning strategy considers each learning problem as a single unit and does not incorporate information associated with the tasks that are closely related with it. Such anomalies have been rectified in the Multi-Task learning (MTL) paradigm, where the model learns a modeling problem, by making use of its associated tasks. Such modeling strategies improve the generalization capacity of the model. In this paper we proposed a mathematical framework for multi-task learning using kernel online learning approach. We applied the proposed algorithm on a synthetic dataset as well as real time data set and the results were found to be promising.