TEEMAL, an adaptive training procedure for a two layer system of linear threshold elements Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/n870zw34z

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • In many practical applications of learning systems to problems of pattern recognition it has been realized and explicitly noted in the literature that linear discriminations are inadequate. On the other hand, it has also been noted that very little is known about the training of non-linear systems. A reasonable compromise between linearity and high complexity is what is called a 'committee machine,' i.e. a collection of linear systems each performing a linear threshold function (subject to adaptation) with an overall element (as the majority rule) to express the final diagnosis. In this paper we will present a system of algorithms which effectively locates a committee machine which uses majority or veto logic. The algorithms are error-correction techniques, which in general perform as many adjustments in training as known algorithms, but with the added feature that in some cases the algorithms will allow the machine to misjudge some samples which are deemed to be noisy or otherwise abnormal without implementing, in relation to these samples, significant change in the committee members. Experimental results are presented using artificially generated data in 2-space, hand-printed letters A and R (Munson), disconnected-connected 3 x 3 arrays, absence-presence of the code 1101, and 3 x 3 quasi-randomly generated arrays (Michalski).
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
Language
Digitization Specifications
  • File scanned at 300 ppi (Monochrome) using ScandAll PRO 1.8.1 on a Fi-6670 in PDF format. CVista PdfCompressor 4.0 was used for pdf compression and textual OCR.
Replaces
Additional Information
  • description.provenance : Made available in DSpace on 2010-08-13T20:13:13Z (GMT). No. of bitstreams: 1 MuellerThomasJ1973.pdf: 12282088 bytes, checksum: cc3da32ec59990d0e497e369979a7325 (MD5)
  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2010-08-13T20:13:13Z (GMT) No. of bitstreams: 1 MuellerThomasJ1973.pdf: 12282088 bytes, checksum: cc3da32ec59990d0e497e369979a7325 (MD5)
  • description.provenance : Approved for entry into archive by Patricia Black(patricia.black@oregonstate.edu) on 2010-08-13T20:11:59Z (GMT) No. of bitstreams: 1 MuellerThomasJ1973.pdf: 12282088 bytes, checksum: cc3da32ec59990d0e497e369979a7325 (MD5)
  • description.provenance : Submitted by Nitin Mohan (mohanni@onid.orst.edu) on 2010-08-11T23:28:21Z No. of bitstreams: 1 MuellerThomasJ1973.pdf: 1563820 bytes, checksum: c67d3f0ae5114e17d369de9363335fcb (MD5)
  • description.provenance : Submitted by Nitin Mohan (mohanni@onid.orst.edu) on 2010-08-13T20:01:51Z No. of bitstreams: 1 MuellerThomasJ1973.pdf: 12282088 bytes, checksum: cc3da32ec59990d0e497e369979a7325 (MD5)
  • description.provenance : Rejected by Patricia Black(patricia.black@oregonstate.edu), reason: See Patti before fixing this one. on 2010-08-12T15:44:41Z (GMT)

Relationships

In Administrative Set:
Last modified: 10/21/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items