Multi-class Multi-scale Stacked Sequential Learning

  • Eloi Puertas
  • Sergio Escalera
  • Oriol Pujol
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6713)


One assumption in supervised learning is that data is independent and identically distributed. However, this assumption does not hold true in many real cases. Sequential learning is that discipline of machine learning that deals with dependent data.

In this paper, we revise the Multi-Scale Sequential Learning approach (MSSL) for applying it in the multi-class case (MMSSL). We have introduced the ECOC framework in the MSSL base classifiers and a formulation for calculating confidence maps from the margins of the base classifiers. Another important contribution of this papers is the MMSSL compression approach for reducing the number of features in the extended data set. The proposed methods are tested on 5-class and 9-class image databases.


Sequential Learning Neighborhood Function Support Lattice Output Code Extended Dataset 
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 2011

Authors and Affiliations

  • Eloi Puertas
    • 1
    • 2
  • Sergio Escalera
    • 1
    • 2
  • Oriol Pujol
    • 1
    • 2
  1. 1.Dept. Matemàtica Aplicada i AnàlisiUniversitat de BarcelonaBarcelonaSpain
  2. 2.Computer Vision Center, Campus UABBellaterraSpain

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