Seminar on Computational Learning and
Adaptation
Lessons for the Computational Discovery of Scientific Knowledge
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 review early analyses of machine learning applications,
along with more recent treatments of successful discoveries of scientific
knowledge. Although the two problem areas have much in common, I use
recent work on computational discovery in Earth science and microbiology
to illustrate some important differences. The lessons that emerge from
these efforts run counter to some rhetorical claims and assumptions
that are widespread in the machine learning and data mining communities.
For example, for many scientific problems, it is more desirable to revise
models than to construct them from scratch, as emphasized by most data
mining researchers. Another difference is that scientific data are
often rare rather than plentiful, despite traditional claims by the
data mining community about the abundance of data. These observations
and others suggest the need to explore research paths which are quite
distinct from those that currently dominate the field.
This talk repeats material from a presentation at the ICML-2002 Workshop
on Data Mining Lessons Learned. The associated paper is available
here.
Date: Thursday, September 26
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Time: 4:15-5:30PM
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Place: Cordura 100
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