Neural Nets pp 147-156 | Cite as

Granular Regression

  • B. Apolloni
  • D. Iannizzi
  • D. Malchiodi
  • W. Pedrycz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3931)


We augment a linear regression procedure by a thruth-functional method in order to identify a highly informative regression line. The idea is to use statistical methods to identify a confidence region for the line and exploit the structure of the sample data falling in this region for identifying the most fitting line. The fitness function is related to the fuzziness of the sampled points as a natural extension of the statistical criterion ruling the identification of the confidence region within the Algorithmic Inference approach. We tested the method on three well known benchmarks.


Support Vector Machine Membership Function Gradient Descent Algorithm Radial Basis Function Information Granule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • B. Apolloni
    • 1
  • D. Iannizzi
    • 2
  • D. Malchiodi
    • 1
  • W. Pedrycz
    • 3
  1. 1.Dipartimento di Scienze dell’InformazioneUniversità degli Studi di MilanoMilanoItaly
  2. 2.Dipartimento di Matematica “F. Enriques”Università degli Studi di MilanoMilanoItaly
  3. 3.Department of Electrical and Computer EngineeringUniversity of Alberta, ECERFEdmontonCanada

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