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Guiding Local Regression Using Visualisation

  • Dharmesh M. Maniyar
  • Ian T. Nabney
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3635)

Abstract

Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.

Keywords

Input Space Domain Expert Local Regression Synthetic Dataset Normalise Mean Square Error 
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 2005

Authors and Affiliations

  • Dharmesh M. Maniyar
    • 1
  • Ian T. Nabney
    • 1
  1. 1.Neural Computing Research GroupAston UniversityBirminghamUK

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