paper available (OCR, discriminant tangent distance)

[Holger Schwenk]

                 **DO NOT FORWARD TO OTHER GROUPS**

FTP-host: ftp.robo.jussieu.fr
FTP-filename: /pub/papers/schwenk.icann95.ps.gz  (6 pages, 31k)


The following paper, published in International Conference on Artificial
Neural Networks (ICANN*95), Springer Verlag, is available via anonymous
FTP at the above location. The paper is 6 pages long.

Sorry, no hardcopies available.


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                   H. Schwenk and M. Milgram 

                          PARC - boite 164
                 Universite Pierre et Marie Curie
                        4, place Jussieu 
                   75252 Paris cedex 05, FRANCE



                              ABSTRACT


 Transformation invariance is known to be fundamental for excellent
 performances in pattern recognition. One of the most successful approach
 is tangent distance, originally proposed for a nearest-neighbor algorithm
 (Simard et al.,1995). The resulting classifier, however, has a very high
 computational complexity and, perhaps more important, lacks discrimination
 capabilities.
 We present a discriminant learning algorithm for a modular classifier based
 on several autoassociative neural networks. Tangent distance as objective
 function guarantees efficient incorporation of transformation invariance.
 The system achieved a raw error rate of 2.6% and a rejection rate of 3.6%
 on the NIST uppercase letters.


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FTP instructions:

unix> ftp ftp.robo.jussieu.fr
Name: anonymous
Password: your full email address
ftp> cd pub/papers
ftp> bin
ftp> get schwenk.icann95.ps.gz
ftp> quit
unix> gunzip schwenk.icann95.ps.gz
unix> lp schwenk.icann95.ps  (or however you print postscript)


I welcome your comments.

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Holger Schwenk
PARC - boite 164                         tel: (+33 1) 44.27.63.08
Universite Pierre et Marie Curie         fax: (+33 1) 44.27.62.14
4, place Jussieu                          
75252 Paris cedex 05                   email: schwenk at robo.jussieu.fr
FRANCE
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