multi-Task Feature Learning

Introduction

It has been both empirically as well as theoretically shown that learning multiple related tasks simultaneously often significantly improves performance as compared to learning each task independently. Learning multiple related tasks simultaneously is especially beneficial when we only have a few data per task; and this benefit comes from pooling together data across many related tasks.