Machine Learning for Science: State of the Art and Future Prospects

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...

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Vydáno v:	Science (American Association for the Advancement of Science) Ročník 293; číslo 5537; s. 2051 - 2055
Hlavní autoři:	Mjolsness, Eric, DeCoste, Dennis
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States American Society for the Advancement of Science 14.09.2001 American Association for the Advancement of Science The American Association for the Advancement of Science
Témata:	Active learning Aerospace Education Algorithm Algorithms Analysis Animals Artificial Intelligence Astronomical Phenomena Astronomy Automation Bioinformatics Biotechnology Cluster Analysis Computational Biology Computer pattern recognition Computer Simulation Computers Cytology Evaluation Gene Expression Profiling Gene Expression Regulation Genes Genomes Genomics Geology Hypotheses (Scientific method) Hypothesis Image Processing, Computer-Assisted Inference Inferences Learning Machine learning Methods Modeling Multidimensional Scaling Neural Networks (Computer) Observation Pattern Recognition Physical Phenomena Physics Principals Robotics Science Scientific method Scientific Methodology Sequencing Space research Statistical Inference Statistics Statistics (Data) Viewpoints Visualization United States
ISSN:	0036-8075, 1095-9203
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Popis
Shrnutí:	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.
Bibliografie:	SourceType-Scholarly Journals-1 ObjectType-Commentary-1 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2
ISSN:	0036-8075 1095-9203
DOI:	10.1126/science.293.5537.2051