Meta-learning with kernels and similarity functions for pla...

Meta-learning with kernels and similarity functions for planning of data mining workflows

inproceedings
Abraham Bernstein, Alexandros Kalousis, Melanie Hilario
Abraham Bernstein
We propose an intelligent data mining (DM) assistant that will combine planning and meta-learning to provide support to users of virtual DM laboratory. A knowledge-driven planner will rely on a data mining ontology to plan the knowledge discovery workflow and determine the set of valid operators for each step of this workflow. A probabilistic meta-learner will select the most appropriate operators by using relational similarity measures and kernel functions over records of past sessions meta-data stored in a DM experiments repository.
Meta-learning with kernels and similarity functions for planning of data mining workflows
2008
Proceedings of the ICML/COLT/UAI 2008 Planing to Learn Workshop
July
ddis