Adaptive automatic classification on the Web

Author(s):  
C. Jenkins ◽  
D. Inman
Author(s):  
Rudy Prabowo ◽  
Mike Jackson ◽  
Peter Burden ◽  
Heinz-Dieter Kno¨ll

This paper presents an ongoing project which enhances the design and implementation of the automatic classifier for classifying the Web pages, known as Automatic Classification Engine (ACE). The enhancement focuses on the use of the ontologies of the domains to carry out classification. To articulate the underlying theories of an ontology, the meaning of a concept, a terminology and a gestalt instance is elucidated. The enhancement results in better classification in terms of accuracy.


Author(s):  
Paul DeCosta ◽  
Kyugon Cho ◽  
Stephen Shemlon ◽  
Heesung Jun ◽  
Stanley M. Dunn

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).


2008 ◽  
Vol 11 (2) ◽  
pp. 83-85
Author(s):  
Howard Wilson
Keyword(s):  

2005 ◽  
Vol 8 (1) ◽  
pp. 16-18
Author(s):  
Howard F. Wilson
Keyword(s):  

1999 ◽  
Vol 3 (2) ◽  
pp. 6-6
Author(s):  
Barbara Shadden
Keyword(s):  

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