Introduction to the Special Issue on Meta-Learning
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.
Cite
Text
Giraud-Carrier et al. "Introduction to the Special Issue on Meta-Learning." Machine Learning, 2004. doi:10.1023/B:MACH.0000015878.60765.42Markdown
[Giraud-Carrier et al. "Introduction to the Special Issue on Meta-Learning." Machine Learning, 2004.](https://mlanthology.org/mlj/2004/giraudcarrier2004mlj-introduction/) doi:10.1023/B:MACH.0000015878.60765.42BibTeX
@article{giraudcarrier2004mlj-introduction,
title = {{Introduction to the Special Issue on Meta-Learning}},
author = {Giraud-Carrier, Christophe G. and Vilalta, Ricardo and Brazdil, Pavel},
journal = {Machine Learning},
year = {2004},
pages = {187-193},
doi = {10.1023/B:MACH.0000015878.60765.42},
volume = {54},
url = {https://mlanthology.org/mlj/2004/giraudcarrier2004mlj-introduction/}
}