Abstract. Recent advances in meta-learning are providing the foundations to construct meta-learning assistants and task-adaptive learners. The goal of this special issue is to foster an interest in meta-learning by compiling representative work in the field. The contributions to this special issue provide strong insights into the construction of future meta-learning tools. In this introduction we present a common frame of reference to address work in meta-learning through the concept of meta-knowledge. We show how meta-learning can be simply defined as the process of exploiting knowledge about learning that enables us to understand and improve the performance of learning algorithms.
As pointed out before, in this work, we use the term meta-learning meaning the automatic process of obtaining knowledge that relates the performance of learning algorithms to the characteristics of the learning problems REF .
Introduction to the Special Issue on Meta-Learning
Machine Learning
Machine Learning
Computer Science