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Framework for the Interactive Learning of Artificial Neural Networks

  • Matúš Užák
  • Rudolf Jakša
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)

Abstract

We propose framework for interactive learning of artificial neural networks. In this paper we study interaction during training of visualizable supervised tasks. If activity of hidden node in network is visualized similar way as are network outputs, human observer might deduce the effect of this particular node on the resulting output. We allow human to interfere with the learning process of network, thus he or she can improve the learning performance by incorporating his or her lifelong experience. This interaction is similar to the process of teaching children, where teacher observes their responses to questions and guides the process of learning. Several methods of interaction with neural network training are described and demonstrated in the paper.

Keywords

Neural Network Hide Layer Human Observer Multilayer Perceptron Individual Neuron 
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 2006

Authors and Affiliations

  • Matúš Užák
    • 1
  • Rudolf Jakša
    • 1
  1. 1.Department of Cybernetics and Artificial IntelligenceTechnical University of KošiceSlovakia

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