Flexible Integration of Multiple Learning Methods into a Problem Solving Architecture
Abstract
One of the key issues in so-called multi-strategy learning systems is the degree of freedom and flexibility with which different learning and inference components can be combined. Most of multi-strategy systems only support fixed, tailored integration of the different modules for a specific domain of problems. We will report here our current research on the Massive Memory Architecture (MMA), an attempt to provide a uniform representation framework for inference and learning components supporting flexible, multiple combination of these components. Rather than a specific combination of learning methods, we are interested in an architecture adaptable to different domains where multiple learning strategies (combinations of learning methods) can be programmed or even learned.
Cite
Text
Plaza and Arcos. "Flexible Integration of Multiple Learning Methods into a Problem Solving Architecture." European Conference on Machine Learning, 1994. doi:10.1007/3-540-57868-4_84Markdown
[Plaza and Arcos. "Flexible Integration of Multiple Learning Methods into a Problem Solving Architecture." European Conference on Machine Learning, 1994.](https://mlanthology.org/ecmlpkdd/1994/plaza1994ecml-flexible/) doi:10.1007/3-540-57868-4_84BibTeX
@inproceedings{plaza1994ecml-flexible,
title = {{Flexible Integration of Multiple Learning Methods into a Problem Solving Architecture}},
author = {Plaza, Enric and Arcos, Josep Lluís},
booktitle = {European Conference on Machine Learning},
year = {1994},
pages = {403-406},
doi = {10.1007/3-540-57868-4_84},
url = {https://mlanthology.org/ecmlpkdd/1994/plaza1994ecml-flexible/}
}