Introduction to the Special Issue on Meta-Learning

作者:Christophe Giraud-Carrier, Ricardo Vilalta, Pavel Brazdil

摘要

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.

论文关键词:meta-learning, meta-knowledge, inductive bias, dynamic bias selection

论文评审过程:

论文官网地址:https://doi.org/10.1023/B:MACH.0000015878.60765.42