A Computationally and Cognitively Plausible Model of Supervised and Unsupervised Learning

  • David M. W. Powers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7888)


Both empirical and mathematical demonstrations of the importance of chance-corrected measures are discussed, and a new model of learning is proposed based on empirical psychological results on association learning. Two forms of this model are developed, the Informatron as a chance-corrected Perceptron, and AdaBook as a chance-corrected AdaBoost procedure. Computational results presented show chance correction facilitates learning.


Chance-corrected evaluation Kappa Perceptron AdaBoost 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • David M. W. Powers
    • 1
    • 2
  1. 1.CSEM Centre for Knowledge & Interaction TechnologyFlinders UniversityAdelaideAustralia
  2. 2.Beijing Municipal Lab for Multimedia & Intelligent SoftwareBJUTBeijingChina

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