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Calibrating recurrent sliding window classifiers for sequential supervised learning

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:

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Calibrating recurrent sliding window classifiers for sequential supervised learning

Creator:

Joshi, Saket Subhash

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 sequence plus some number...

Resource Type:

Masters Thesis