Call for papers, IEEE Trans. NNs Special Issues on ANNs and PR

[mao@almaden.ibm.com]

                           CALL  FOR  PAPERS

                   IEEE Transactions on Neural Networks

                              Special Issue on
             Artificial Neural Networks and Pattern Recognition

                 Tentative Publication Date: November 1996


Artificial neural networks (ANN) have now been recognized as powerful
and economical tools for solving a large variety of problems in a number
of scientific and engineering disciplines. The literature on neural
networks is enormous consisting of a large number of books, journals and
conference proceedings, and new commercial software and hardware products.
A large portion of the research and development on ANNs is devoted to solving
pattern recognition problems. Pattern recognition (PR) is a relatively
mature discipline. Over the past 50 years, a number of different paradigms
(statistical, syntactic and neural networks) have been utilized for solving
a variety of recognition problems. But, real-world recognition problems are
sufficiently difficult so that a single paradigm is not "optimal" for
different recognition problems. As a result, successful recognition systems
based either on statistical approach or neural networks exist in limited
domains (e.g., handprinted character recognition and isolated word speech
recognition).

There is a close relationship between some of the popular ANN models and
statistical pattern recognition (SPR) approaches. Quite often, these
relationships are either not known to researchers or not fully exploited to
build "hybrid" recognition systems. In spite of this close resemblance between
ANN and SPR, ANNs have provided a variety of novel or supplementary approaches
for pattern recognition tasks. More noticeably, ANNs have provided
architectures on which many classical SPR algorithms (e.g., tree classifiers,
principal component analysis, K-means clustering) can be mapped to facilitate
hardware implementation. On the other hand, ANNs can derive benefit from
some well-known results in SPR (e.g., Bayes decision theory, nearest neighbor
rules, curse of dimensionality and Parzen window classifier).

The purpose of this special issue is to increase the awareness of researchers
and practitioners of pattern recognition about the common links between ANNs
and SPR. This is likely to lead to more communication and cooperative work
between the two research communities. Such an effort will not only avoid
repetitious work but, more importantly, will stimulate and motivate individual
disciplines. It is our hope that this special issue will lead to a synergistic
approach which combines the strengths of ANN and SPR in order to achieve a
significantly better performance for complex pattern recognition problems.

Specific topics of interest include, but are not limited to:

   o  Old and new links between ANNs and SPR (e.g., Adaptive Mixture of
          Expert (AME) and Hierarchical Mixture of Experts (HME) versus traditional
          decision trees, recurrent ANNs and time-delay ANNs versus Hidden Markov
          Models, generalization ability in ANNs versus curse of dimensionality).

   o  Comparative studies of ANN and SPR approaches that lead to useful
          guidelines in practice (e.g., under what conditions does one approach
          exhibit superiority to the other?).

   o  New ANN models for PR.

          -- representation/feature extraction (compression rate, invariance,
                         robustness, and efficiency) using ANNs.
          -- supervised classification.
          -- clustering/unsupervised classification.

   o  Combination of ANN and SPR classifiers/estimators, and features extracted
          using traditional PR approaches and ANNs.

   o  Hybrid (using ANNs and traditional PR approaches) systems for solving
          real-world PR problems (e.g., face recognition, cursive handwriting
          recognition, and speech recognition).

Although these topics cover a broad area of research, we encourage papers that
explore the relationship between ANNs and traditional PR. Authors should relate
their work with both the PR and ANN literature. Papers should also emphasize
results that have been or can be potentially applied to "real world" applications;
they should include evaluations through either experimentation, simulation,
analysis and/or experience.

Guest Editors:
--------------
Professor Anil K. Jain                          Dr. Jianchang Mao
Department of Computer Science                  Image and Multimedia Systems, DPE/803
A714 Wells Hall                                 IBM Almaden Research Center
Michigan State University                       650 Harry Road
East Lansing, MI 48824, USA                     San Jose, CA 95120, USA
Email: jain at cps.msu.edu                         Email: mao at almaden.ibm.com
Fax: 517-432-1061                               Fax: 408-927-3497

Instructions for submitting papers:
-----------------------------------
Manuscripts must not have been previously published or currently submitted
for publication elsewhere.  Each manuscript should be no more than 35 pages
(double space, 12 point font) including all text, references, and illustrations.
Each copy of the manuscript should include a title page containing title,
authors' names and affiliations, postal and email addresses, telephone numbers
and Fax numbers, a 300-word abstract and a list of keywords identifying the
central issues of the manuscript's contents. Please submit six copies of your
manuscript to either of the guest editors by January 5, 1996.
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