Minimal Correlation Classification

  • Noga Levy
  • Lior Wolf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)

Abstract

When the description of the visual data is rich and consists of many features, a classification based on a single model can often be enhanced using an ensemble of models. We suggest a new ensemble learning method that encourages the base classifiers to learn different aspects of the data. Initially, a binary classification algorithm such as Support Vector Machine is applied and its confidence values on the training set are considered. Following the idea that ensemble methods work best when the classification errors of the base classifiers are not related, we serially learn additional classifiers whose output confidences on the training examples are minimally correlated. Finally, these uncorrelated classifiers are assembled using the GentleBoost algorithm. Presented experiments in various visual recognition domains demonstrate the effectiveness of the method.

Keywords

Covariance Cyan Vanilla 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Noga Levy
    • 1
  • Lior Wolf
    • 1
  1. 1.The Blavatnik School of Computer ScienceTel Aviv UniversityIsrael

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