A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data

TLDR

Semi‑supervised learning seeks to improve supervised algorithms by incorporating unlabeled data, yet its overall effectiveness remains incompletely understood. The study aims to learn predictive structures across multiple tasks to develop a novel semi‑supervised learning framework. The authors formulate a general theoretical framework for structural learning across tasks, propose algorithms, and analyze computational aspects.

Abstract

One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semi-supervised learning. Although a number of such methods are proposed, at the current stage, we still don't have a complete understanding of their effectiveness. This paper investigates a closely related problem, which leads to a novel approach to semi-supervised learning. Specifically we consider learning predictive structures on hypothesis spaces (that is, what kind of classifiers have good predictive power) from multiple learning tasks. We present a general framework in which the structural learning problem can be formulated and analyzed theoretically, and relate it to learning with unlabeled data. Under this framework, algorithms for structural learning will be proposed, and computational issues will be investigated. Experiments will be given to demonstrate the effectiveness of the proposed algorithms in the semi-supervised learning setting.