Neural Computing & Applications

, Volume 13, Issue 3, pp 229–236 | Cite as

A generalised regression algorithm for Web page categorisation

  • Ioannis Anagnostopoulos
  • Christos Anagnostopoulos
  • George Kouzas
  • Dimitrios D. Vergados
Original Article


This paper proposes an information system that classifies Web pages according a taxonomy, which is mainly used from seven search engines/directories. The proposed classifier is a four-layer generalised regression neural network (GRNN) that aims to perform the information segmentation according to information filtering techniques using content descriptor vectors. Eight categories of Web pages were used in order to evaluate the robustness of the method, while no restrictions were imposed except for the language of the content, which is English. The system can be used as an assistant and consultative tool for classification purposes as well as for estimating the population of Web pages at any given point in time.


Neural network Web page classification GRNN 

List of symbols


Normalised frequency of term k


Inverse document frequency of term k


Tag hierarchical rating

\(\bar x\)

Mean value


Variance (distributions of normalised and inverse document frequencies over the terms’ rank order)


The probability density function (pdf) of the vector random variable x and its scalar random variable z


The Euclidean distance between vector random variable x and sample points x i

\( \bar \sigma \)

A width parameter, which satisfies the asymptotic behaviour as the number of Parzen windows becomes large


The ‘beta’ coefficient for all the local approximators in the middle layer of the proposed neural network classifier



The authors are very appreciative of all anonymous expert editors of Liaison Systems S.A. for their provided help as far as the validation of the Web pages, used for training and testing the GRNN classifier. The authors would also like to recognise the partial financial support from the European Community concerning the project ERMIS (Electronic commeRce Measurements through Intelligent agentS, IST-1999-21051), in which an initial version of the proposed GRNN classifier was tested and validated.


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Copyright information

© Springer-Verlag London Limited 2004

Authors and Affiliations

  • Ioannis Anagnostopoulos
    • 1
  • Christos Anagnostopoulos
    • 2
  • George Kouzas
    • 1
  • Dimitrios D. Vergados
    • 3
  1. 1.School of Electrical and Computer Engineering, Department of Communication, Electronics and Information SystemsNational Technical University of AthensAthensGreece
  2. 2.Department of Cultural Technology and CommunicationsUniversity of the AegeanMytilini-LesvosGreece
  3. 3.Department of Information and Communication Systems EngineeringUniversity of the AegeanKarlovassi-SamosGreece

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