Multi-stage Learning of Linear Algebra Algorithms

In evolving applications, there is a need for the dynamic selection of algorithms or algorithm parameters. Such selection is hardly ever governed by exact theory, so intelligent recommender systems have been proposed. In our application area, the iterative solution of linear systems of equations, th...

Full description

Saved in:
Bibliographic Details
Published in2008 Seventh International Conference on Machine Learning and Applications pp. 402 - 407
Main Authors Eijkhout, V., Fuentes, E.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2008
Subjects
Eigenvalues and eigenfunctions
Heuristic algorithms
Intelligent systems
Iterative algorithms
Learning systems
Linear algebra
Linear systems
Machine learning
Machine learning algorithms
Measurement
multi-stage recommendations
Online AccessGet full text

Cover

More Information
Summary:In evolving applications, there is a need for the dynamic selection of algorithms or algorithm parameters. Such selection is hardly ever governed by exact theory, so intelligent recommender systems have been proposed. In our application area, the iterative solution of linear systems of equations, the recommendation process is especially complicated, since the classes have a multi-dimensional structure. We discuss different strategies of recommending the different components of the algorithms.
ISBN:0769534953
9780769534954
DOI:10.1109/ICMLA.2008.10