No subject

[Isabelle Guyon]

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                           SPECIAL ISSUE 
         
                               OF THE

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE

				ON
                     
                          NEURAL NETWORKS

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ISSN: 0218-0014
Advances in Pattern Recognition Systems using Neural Networks,
Eds. I. Guyon and P.S.P. Wang, IJPRAI, vol. 7, number 4, August 1993.

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Among the many applications that have been proposed for neural networks,
pattern recognition has been one of the most successful ones, why? 
This collection of papers give will satisfy your curiosity!

The commonplace rationale behind using Neural Networks is that a machine which
architecture imitates that of the brain should inherit its remarquable
intelligence. This logic usually contrasts with the reality of the performance
of Neural Networks. In this special issue, however, the authors have kept some
distance with the biological foundations of Neural Networks. The success of
their applications relies, to a large extend, on careful engineering. For
instance, many novel aspects of the works presented here are concerned with
combining Neural Networks with other ``non neural'' modules.

With:

  [   [1] ]  Y. Bengio. 
    A Connectionist Approach to Speech Recognition.  

  [   [2] ]
 J. Bromley, J. W. Bentz, L. Bottou, I. Guyon, L. Jackel, Y. Le Cun, C. Moore,
E. Sackinger, and R. Shah. 
    Signature Verification with a Siamese TDNN.  

  [   [3] ]
 C. Burges, J. Ben, Y. Le Cun, J. Denker and C. Nohl. 
    Off-line Recognition of Handwritten Postal Words using Neural Networks.  

  [   [4] ]
 H. Drucker, Robert Schapire and Patrice Simard. 
    Boosting Performance in Neural Networks.  

  [   [5] ]
 F. Fogelman, B. Lamy and E. Viennet. 
    Multi-Modular Neural Network Architectures for Pattern Recognition:
Applications in Optical Character Recognition and Human Face Recognition.  

  [   [6] ]
 A. Gupta, M. V. Nagendraprasad, A. Liu, P. S. P. Wang and S. Ayyadurai. 
    An Integrated Architecture for Recognition of
Totally Unconstrained Handwritten Numerals.  

  [   [7] ]
 E. K. Kim, J. T. Wu, S. Tamura, R. Close, H. Taketani, H. Kawai, M. Inoue and K.
Ono. 
    Comparison of Neural Network and K-NN Classification Methods in Vowel
and Patellar Subluxation Image Recognitions.  

  [   [8] ]
 E. Levin, R. Pieraccini and E. Bocchieri. 
    Time-Warping Network: A Neural Approach to Hidden Markov Model based
Speech Recognition.  

  [   [9] ]
 H. Li and J. Wang. 
    Computing Optical Flow with a Recurrent Neural Network.  

  [   [10] ]
 W. Li and N. Nasrabadi. 
    Invariant Object recognition Based on Neural Network of Cascaded RCE Nets.  

  [   [11] ]
 G. Martin, M. Rashid and J. Pittman. 
    Integrated Segmentation and Recognition Through Exhaustive Scans or
Learned Saccadic Jumps.  

  [   [12] ]
 C. B. Miller and C. L. Giles. 
    Experimental Comparison of the Effect of Order in Recurrent Neural Networks.  

  [   [13] ]
 L. Miller and A. Gorin. 
    Structured Networks, for Adaptive Language Acquisition.  

  [   [14] ]
 N. Morgan, H. Bourlard, S. Renals M. Cohen and H. Franco. 
    Hybrid Neural Network / Hidden Markov Model Systems for Continuous Speech
Recognition.  

  [   [15] ]
 K. Peleg and U. Ben-Hanan. 
    Adaptive Classification by Neural Net Based Prototype Populations.  

  [   [16] ]
 L. Wiskott and C. von der Malsburg. 
    A Neural System for the Recognition of Partially Occluded Objects in
Cluttered Scenes - A Pilot Study.  

  [   [17] ]
 G. Zavaliagkos, S. Austin, J. Makhoul and R. Schwartz. 
    A Hybrid Continuous Speech Recognition System Using Segmental Neural
Nets with Hidden Markov Models.