Seminar on Computational Learning and
Adaptation
Inducing Process Models from Continuous Data
Pat Langley
Institute for the Study of Learning and Expertise, and
Center for the Study of Language and Information, Stanford University
langley@isle.org
http://newatlantis.isle.org/~langley
In this talk, I pose a novel research problem for machine learning
that involves constructing a quantitative process model from numeric
data. I claim that casting learned knowledge in terms of processes
with associated equations is desirable for scientific and engineering
domains, where such notations are commonly used. I also argue that
existing induction methods are not well suited to this task, although
some techniques hold partial solutions. In response, I describe an
approach to learning process models from time-series data and then
illustrate its behavior in a population dynamics domain. In closing,
I describe open issues in process model induction and encourage other
researchers to tackle this important problem.
This talk describes work done jointly with Javier Sanchez, Ljupco
Todorovski, and Saso Dzeroski. Details are available in the paper
http://www.isle.org/~langley/papers/process.icml02.ps
which will appear in the Proceedings of the Nineteenth International
Conference on Machine Learning.
Date: Thursday, May 30
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Time: 4:15-5:30PM
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Place: Ventura 17
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