Machine learning for science: state of the art and future prospects.

By Eric Mjolsness, Dennis DeCoste. Science, 2001

349 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c6efa1ce0177d6990ad6580013e04b281c4a5295

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Citations in the corpus

Nonnegative Least-Correlated Component Analysis for Separation of Dependent Sources by Volume Maximization (2010, background): “…decomposition is a popular technique widely used in many branches of science and engineering, blind source separation (BSS) deals with a real-world situation where neither the sources nor the mixing matrix is known and the only available information comes from the mixed observations [1], [2].”
Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis (2016, positive): “This feature of ML methods is particularly important, given the ease with which the machine can seamlessly incorporate new data to continually update and optimize its algorithm—and thus continually improve its predictive performance over time.(5,26)”
Support vector machines for temporal classification of block design fMRI data (2005, background): “…advances in the interrelated fields of machine learning, data mining, and statistics have enhanced our capabilities to extract and characterize subtle features in data sets from a wide variety of scientific fields (Cherkassky and Mulier, 1998; Hastie et al., 2001; Mjolsness and DeCoste, 2001).”
TRY – a global database of plant traits (2011, background): “An integra- tive exploration of ecological and biogeographical infor- mation in TRY is expected to substantially benefit from r 2011 Blackwell Publishing Ltd, Global Change Biology, 17, 2905–2935 progress in the science of machine learning and pattern recognition (Mjolsness & DeCoste, 2001).”
Evolutionary Computation Meets Machine Learning: A Survey (2011, background): “ML is one of the most promising and salient research areas in artificial intelligence, which has experienced a rapid development and has become a powerful tool in a wide range of applications [ 8 ].”
Support vector machines for predicting distribution of Sudden Oak Death in California (2005, methodology): “…combined with sophisticated statistical methods, machine learning methods have been increasingly used and shown as powerful tools in a wide variety of science disciplines including planetary science, computer science, bioinformatics, and environmental science (Mjolsness and DeCoste, 2001).”