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A study of the similarities between topics

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Abstract

This paper presents a new methodology whereby the relationships among points of interest appearing in Internet Web pages may be graphically represented. This is done by quantifying the various points and representing them graphically using an intuitive interpretation of the similarity of sets. The similarity or interrelationships existing among various lines of research may be studied based on information taken from the Web pages using function taken from Learning Theory.

A visual analysis of those interrelationships has been applied to the field of machine learning during the time interval May 2003–May 2004.

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Notes

  1. 1This section follows from Skhölkopf (2000).

  2. 2The random variable X = has a Bernoulli(p) distribution with p = P(A).

  3. 3The transpose of a vector c is denoted by c′.

  4. 4The properties are demonstrated in González (2002).

  5. 5To interpret this graphic representation and the next it should be understood that the various items are arranged as a function of their respective probabilities.

  6. 6The order in which the items are represented goes fom left to right and from up to down.

References

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Acknowledgements

This work has been partially funded by the Spanish Ministerio de Ciencia y Tecnología under grant DPI2003-07146-C02-01, the European Regional Development Fund (ERDF/FEDER), and ACPAI-2003/014 awarded by Junta de Andalucía (Spain).

The authors would like to thank the reviewers for their suggestions in improving the readability of the paper.

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Authors and Affiliations

  1. Department of Applied Economy I, University of Seville, Avda Ramón y Cajal s/n, C.P., 41008, Seville, Spain

    Luis González & Francisco Velasco

  2. Department of Computer Languages and Systems, University of Seville, Avda Reina Mercedes s/n, C.P., 41012., Seville, Spain

    Rafael M. Gasca

Authors
  1. Luis González
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  2. Francisco Velasco
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  3. Rafael M. Gasca
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González, L., Velasco, F. & Gasca, R.M. A study of the similarities between topics. Computational Statistics 20, 465–479 (2005). https://doi.org/10.1007/BF02741309

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