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An Unsupervised Neural Model to Analyse Thermal Properties of Construction Materials

  • Emilio Corchado
  • Pedro Burgos
  • María del Mar Rodríguez
  • Verónica Tricio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3037)

Abstract

This study proposes a new approach to feature selection and the identification of underlaying factors. The goal of this method is to visualize and extract information from complex and high dimensional data sets. The model proposed is an extension of Maximum Likelihood Hebbian Learning [14], [5], [15] based on a family of cost functions, which maximizes the likelihood of identifying a specific distribution in the data while minimizing the effect of outliers [7], [10]. We demonstrate a hierarchical extension method which provides an interactive method for identifying possibly hidden structure in the dataset. We have applied this method to study the thermal evolution of several construction materials under different thermal and humidity environmental conditions.

Keywords

Principal Component Analysis Support Vector Regression Transitory State Projection Pursuit Vertical Face 
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 2004

Authors and Affiliations

  • Emilio Corchado
    • 1
  • Pedro Burgos
    • 1
  • María del Mar Rodríguez
    • 2
  • Verónica Tricio
    • 2
  1. 1.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  2. 2.Department of PhysicsUniversity of BurgosBurgosSpain

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