Interactive Machine Learning in Data Exploitation
Reid B. Porter, James P. Theiler, Don R. Hush · Computing in Science & Engineering · 2013
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.