Publication Date:
2001-09-15
Description:
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.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Mjolsness, E -- DeCoste, D -- New York, N.Y. -- Science. 2001 Sep 14;293(5537):2051-5.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Machine Learning Systems Group, Jet Propulsion Laboratory/California Institute of Technology, Pasadena, CA, 91109, USA. mjolsness@jpl.nasa.gov〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/11557883" target="_blank"〉PubMed〈/a〉
Keywords:
Algorithms
;
Animals
;
*Artificial Intelligence
;
Astronomical Phenomena
;
Astronomy
;
Cluster Analysis
;
*Computational Biology
;
Computer Simulation
;
Gene Expression Profiling
;
Gene Expression Regulation
;
Image Processing, Computer-Assisted
;
Neural Networks (Computer)
;
Physical Phenomena
;
Physics
;
Robotics
Print ISSN:
0036-8075
Electronic ISSN:
1095-9203
Topics:
Biology
,
Chemistry and Pharmacology
,
Computer Science
,
Medicine
,
Natural Sciences in General
,
Physics
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