Calibrating recurrent sliding window classifiers for sequential supervised learning Public Deposited

http://ir.library.oregonstate.edu/concern/technical_reports/73666580w

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  • 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 of previously predicted items into a prediction for the next item in the sequence. This paper describes a general-purpose implementation of RSW classifiers and discusses the highly practical issue of how to choose the size of the input window and the number of previous predictions to incorporate. Experiments on two real world domains show that the optimal choices vary from one learning algorithm to another. They also depend on the evaluation criterion number of correctly predicted items versus number of correctly predicted whole sequences. We conclude that window sizes must be chosen by cross validation. The results have implications for the choice of window sizes for other models.
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  • description.provenance : Submitted by Laura Wilson (laura.wilson@oregonstate.edu) on 2012-05-30T17:19:21Z No. of bitstreams: 1 Calibrating recurrent sliding window classifiers for sequential supervised learning.pdf: 130072 bytes, checksum: f4ad3bdb905c45a3f277d874068309a1 (MD5)
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2012-05-30T17:20:17Z (GMT) No. of bitstreams: 1 Calibrating recurrent sliding window classifiers for sequential supervised learning.pdf: 130072 bytes, checksum: f4ad3bdb905c45a3f277d874068309a1 (MD5)
  • description.provenance : Made available in DSpace on 2012-05-30T17:20:17Z (GMT). No. of bitstreams: 1 Calibrating recurrent sliding window classifiers for sequential supervised learning.pdf: 130072 bytes, checksum: f4ad3bdb905c45a3f277d874068309a1 (MD5) Previous issue date: 2003

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