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Abstract

For a classification problem that is implicitly represented by a training data set, analysis of data complexity provides a linkage between context and solution. Instead of directly optimizing classification accuracy by tuning the learning algorithms, one may seek changes in the data sources and feature transformations to simplify the data geometry. Simplified class geometry benefits learning in a way common to many methods. We review some early results in data complexity analysis, compare these to recent advances in manifold learning, and suggest directions for further research.

Keywords

Data Complexity Locally Linear Embedding Manifold Learning Class Geometry Data Geometry 
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 2008

Authors and Affiliations

  • Tin Kam Ho
    • 1
  1. 1.Bell Labs, Alcatel-LucentUSA

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