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Creator:: Oregon State University. Department of Computer Science, Joshi, Saket Subhash, and Dietterich, Thomas G.
Abstract:: Sequential supervised learning problems involve assigning a class label to each item in a sequence. Examples include part-of-speech tagging and text-to-speech mapping. A very general-purpose strategy for solving such problems is to construct a recurrent sliding window (R.SW) classifier, which maps some window of the input sequence plus some number...
Resource Type:: Research Paper
Full Text:: Supervised Learning Saket Joshi & Thomas G. Dietterich School of Electrical Engineering and Computer Science

Creator:: Joshi, Saket, Dietterich, Thomas G., and Oregon State University. School of Electrical Engineering and Computer Science
Abstract:: Sequential supervised learning problems involve assigning a class label to each item in a sequence. Examples include part of speech tagging and text to speech mapping. A very general-purpose strategy for solving such problems is to construct a recurrent sliding window (RSW) classifier which maps some window of the input...
Resource Type:: Technical Report
Full Text:: Sequential Supervised Learning Saket Joshi � Thomas G� Dietterich School of Electrical Engineering and

Creator:: Oregon State University. Dept. of Computer Science, Margineantu, Dragos D. (Dragos Dorin), and Dietterich, Thomas G.
Abstract:: Many machine learning applications require classifiers that minimize an asymmetric loss function rather than the raw misclassification rate. We study methods for modifying C4.5 to incorporate arbitrary loss matrices. One way to incorporate loss information into C4.5 is to manipulate the weights assigned to the examples from different classes. For...
Resource Type:: Research Paper
Full Text:: G. Dietterich Institut D'Investigaci�o en Intel�lig�encia Arti�cial, Consejo Superior de

ScholarsArchive@OSU