DocumentCode
880287
Title
Comments on "Optimal training of thresholded linear correlation classifiers" [with reply]
Author
Lovell, David ; Tsoi, A.C. ; Downs, T. ; Hildebrandt, T.H.
Author_Institution
Dept. of Electr. Eng., Queensland Univ., St. Lucia, Qld., Australia
Volume
4
Issue
2
fYear
1993
fDate
3/1/1993 12:00:00 AM
Firstpage
367
Lastpage
369
Abstract
A difficulty with the application of the closed-form training algorithm for the neocognitron proposed by T.H. Hildebrandt (ibid., vol.2, p.557-88, Nov. 1991) is reported. In applying this algorithm the commenters have observed that S-cells frequently fail to respond to features that they have been trained to extract. Results which indicate that this training vector rejection in an important factor in the overall classification performance of the neocognitron trained using Hildebrandt´s procedure are presented. In reply, Hildebrandt explains that the negative results obtained by the commenter are not specific to the proposed algorithm and are easily explained in terms of set theory.<>
Keywords
learning (artificial intelligence); neural nets; pattern recognition; closed-form training; learning; neocognitron; neural nets; pattern recognition; thresholded linear correlation classifiers; Correlators; Feature extraction; Kernel; Partitioning algorithms; Testing;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.207625
Filename
207625
Link To Document