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.
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Giraud-Carrier, C., Vilalta, R. & Brazdil, P. Introduction to the Special Issue on Meta-Learning. Machine Learning 54, 187–193 (2004). https://doi.org/10.1023/B:MACH.0000015878.60765.42
- inductive bias
- dynamic bias selection