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Science 14 September 2001:
Vol. 293. no. 5537, pp. 2051 - 2055
DOI: 10.1126/science.293.5537.2051
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Viewpoint
Machine Learning for Science: State of the Art and Future Prospects
Eric Mjolsness,*
Dennis DeCoste
Recent advances in machine learning methods, along
with successful applications across a wide variety of fields such as
planetary science and bioinformatics, promise powerful new tools for
practicing scientists. This viewpoint highlights some useful
characteristics of modern machine learning methods and their relevance
to scientific applications. We conclude with some speculations on
near-term progress and promising directions.
Machine Learning Systems Group, Jet Propulsion
Laboratory/California Institute of Technology, Pasadena, CA, 91109, USA.
*
To whom correspondence should be addressed. E-mail:
mjolsness{at}jpl.nasa.gov
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