Call for Papers:
Special Issue on PATTERN RECOGNITION IN GRAPHICAL DOMAINS
Neurocomputing is seeking original and unpublished manuscripts for a Special Issue on "Pattern Recognition in Graphical Domains", scheduled for publication in June/July 2008.
Traditional machine learning applications usually cope with graphs by a preprocessing procedure that transforms structured data to simpler representations. This approach relies on what is called the "feature extraction" process, but it turns out to be quite unnatural for several situations where data are intrinsically organized as graphs, i.e. relationships exist among atomic sub-entities. Unfortunately, valuable information may be lost during the preprocessing and, as a consequence, classical methods may suffer from poor performance and generalization. Therefore, recursive or nested representations, as opposed to "flat" attribute-value data organizations, seem to be more adequate for many relevant problems arising from chemistry, bioinformatics, and the World Wide Web.
Recent studies on statistical pattern recognition and neural networks show possible directions to exploit structural information in problems which are inherently of sub-symbolic nature.
This special issue is intended to propose a critical re-thinking of the classic learning approaches and to investigate on possible new methodologies and applications of pattern recognition in graphical domains.
Submitted articles must not have been previously published and must not be currently submitted for publication elsewhere. Topics of interest include, but are not limited to, the following:
Manuscript should follow the standard guidelines for Neurocomputing. Guidelines for formatting papers can be found in the Guide for Authors at http://www.elsevier.com/wps/find/journaldescription.cws_home/505628/authorinstructions. Prospective authors should submit an electronic copy of their complete manuscript through the Elsevier online submission system at http://ees.elsevier.com/neucom/. To ensure that all manuscripts are correctly identified for inclusion into the special issue, authors must select "Pattern Recognition" when they reach the "Article Type" step in the submission process.
University of Siena
University of Siena