No subject

[Yoshio Takane]

This will be the last call for papers for the special issue of 
Behaviormetrika.


*****CALL FOR PAPERS*****

BEHAVIORMETRIKA, an English journal published in Japan to promote
the development and dissemination of quantitative methodology for 
analysis of human behavior, is planning to publish a special issue 
on 

ANALYSIS OF KNOWLEDGE REPRESENTATIONS IN NEURAL NETWORK (NN) 
MODELS 

broadly construed.  I have been asked to serve as the guest 
editor for the special issue and would like to invite all potential 
contributors to submit high quality articles for possible 
publication in the issue.  

In statistics information extracted from the data are stored in 
estimates of model parameters.  In regression analysis, for example, 
information in observed predictor variables useful in prediction is 
summarized in estimates of regression coefficients.  Due to the 
linearity of the regression model interpretation of the estimated 
coefficients is relatively straightforward.  In NN models knowledge 
acquired from training samples is represented by the weights 
indicating the strength of connections between neurons.  However, 
due to the nonlinear nature of the model interpretation of these 
weights is extremely difficult, if not impossible.  Consequently, 
NN models have largely been treated as black boxes.  This special 
issue is intended to break the ground by bringing together various 
attempts to understand internal representations of knowledge 
in NN models.  Papers are invited on network analysis including: 

* Methods of analyzing basic mechanisms of NN models 
* Examples of successful network analysis 
* Comparison among different network architectures in their 
 knowledge representation (e.g., BP vs Cascade Correlation) 
* Comparison with statistical approaches
* Visualization of high dimensional functions 
* Regularization methods to improve the quality of knowledge 
 representation 
* Model selection in NN models 
* Assessment of stability and generalizability of knowledge in NN 
 models
* Effects of network topology, data encoding scheme, algorithm,
 environmental bias, etc. on network performance 
* Implementing prior knowledge in NN models

SUBMISSION:
Deadline for submission: January 31, 1998
Deadline for the first round reviews: April 30, 1998
Deadline for submission of the final version: August 31, 1998
Number of copies of a manuscript to be submitted: four
Format: no longer than 10,000 words; APA style

ADDRESS FOR SUBMISSION:
Professor Yoshio Takane
Department of Psychology
McGill University
1205 Dr. Penfield Avenue 
Montreal QC H3A 1B1 CANADA
email: takane at takane2.psych.mcgill.ca
tel:  514 398 6125   fax:  514 398 4896