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Interactive Machine Learning in Data Exploitation
Sept.-Oct. 2013 (vol. 15 no. 5)
pp. 12-20
Reid Porter, Los Alamos National Lab
James Theiler, Los Alamos National Lab
Don Hush, Los Alamos National Lab
The goal of interactive machine learning is to help scientists and engineers exploit more specialized data from within their deployed environment in less time, with greater accuracy and fewer costs. A basic introduction to the main components is provided here, untangling the many ideas that must be combined to produce practical interactive learning systems. This article also describes recent developments in machine learning that have significantly advanced the theoretical and practical foundations for the next generation of interactive tools.
Index Terms:
Machine learning,Interactive systems,Image segmentation,Vocabulary,Learning systems,Random variables,Data processing,Information processing,scientific computing,machine learning,pattern recognition,interactive systems
Citation:
Reid Porter, James Theiler, Don Hush, "Interactive Machine Learning in Data Exploitation," Computing in Science and Engineering, vol. 15, no. 5, pp. 12-20, Sept.-Oct. 2013, doi:10.1109/MCSE.2013.74
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