Calibrating recurrent sliding window classifiers for sequential supervised learning Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/mg74qp203

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • 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 including hidden Markov models and conditional random fields.
Resource Type
Date Available
Date Copyright
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Academic Affiliation
Non-Academic Affiliation
Subject
Rights Statement
Peer Reviewed
Language
Digitization Specifications
  • File scanned at 300 ppi (Monochrome) using Capture Perfect 3.0 on a Canon DR-9050C in PDF format. CVista PdfCompressor 4.0 was used for pdf compression and textual OCR.
Replaces
Additional Information
  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2012-06-21T18:27:58Z (GMT) No. of bitstreams: 1 JoshiSaketSubhash2004_Redacted.pdf: 481006 bytes, checksum: 69b26190fd295e849fc4403b87e190c3 (MD5)
  • description.provenance : Submitted by Kirsten Clark (kcscannerosu@gmail.com) on 2012-06-19T22:16:49Z No. of bitstreams: 1 JoshiSaketSubhash2004_Redacted.pdf: 481006 bytes, checksum: 69b26190fd295e849fc4403b87e190c3 (MD5)
  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2012-06-21T18:25:07Z (GMT) No. of bitstreams: 1 JoshiSaketSubhash2004_Redacted.pdf: 481006 bytes, checksum: 69b26190fd295e849fc4403b87e190c3 (MD5)
  • description.provenance : Made available in DSpace on 2012-06-21T18:27:58Z (GMT). No. of bitstreams: 1 JoshiSaketSubhash2004_Redacted.pdf: 481006 bytes, checksum: 69b26190fd295e849fc4403b87e190c3 (MD5) Previous issue date: 2003-10-03

Relationships

In Administrative Set:
Last modified: 08/03/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items