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)


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.


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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  1. 1.
    Takagi, H.: Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE 89(9), 1275–1296 (2001)CrossRefGoogle Scholar
  2. 2.
    Weichert, J., Tesauro, G.: Visualizing processes in neural networks. IBM J. Res. Develop. 35, 244 (1991)CrossRefGoogle Scholar
  3. 3.
    Craven, M.W., Shavlik, J.W.: Visualizing learning and computation in artificial neural networks. International Journal on Artificial Intelligence Tools 1, 399–425 (1991)CrossRefGoogle Scholar
  4. 4.
    Olden, J., Jackson, D.: Illuminating the “black box”: Understanding variable contributions in artificial neural networks. Ecological Modelling 154, 135–150 (2002)CrossRefGoogle Scholar
  5. 5.
    Edlund, M., Caudel, T.: Realtime visualization of the learning processes in the lapart neural architecture as it controls a simulated autonomous vehicle. Proceedings of the International Joint Conference on Neural Networks 3, 41 (2000)Google Scholar
  6. 6.
    Streeter, M., Ward, M., Alvarez, S.A.: Nvis: An interactive visualization tool for neural networks. Proceedings of SPIE Symposium on Visual Data Exploration and Analysis 4302(8), 234–241 (2001)Google Scholar
  7. 7.
    Duch, W.: Coloring black boxes: visualization of neural network decision. Proc. of International Joint Conference on Neural Networks (IJCNN) 1, 1735–1740 (2003)CrossRefGoogle Scholar
  8. 8.
    Duch, W.: Visualization of hidden node activity in neural networks: I. visualization methods. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 38–43. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Tzeng, F.Y., Ma, K.L.: Opening the black box - data driven visualization of neural networks. In: Proceedings of IEEE Visualization 2005 Conference, pp. 383–390 (2005)Google Scholar
  10. 10.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company, Inc., New York (1994)MATHGoogle Scholar
  11. 11.
    Užák, M.: Visualization and interaction in the process of neural network learning. Master’s thesis, Technical university of Košice (2005) (in Slovak)Google Scholar
  12. 12.
    Pao, Y.H.: Adaptive pattern recognition and neural networks. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)MATHGoogle Scholar

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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