Non-uniform Initialization of Inputs Groupings in Contextual Neural Networks

  • Maciej HukEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)


Contextual neural networks which are using neurons with conditional aggregation functions were found to be efficient and useful generalizations of classical multilayer perceptrons. They allow to generate neural classification models with good generalization and low activity of connections between neurons in hidden layers. The key factor to build such solutions is achieving self-consistency between continuous values of weights of neurons’ connections and their mutually related non-continuous aggregation priorities. This allows to optimize neuron inputs aggregation priorities by simultaneous gradient-based optimization of connections’ weights with generalized BP algorithm. But such method additionally needs initial setting of connections groupings (scan-paths) to define priorities of signals during first ω epochs of training. In earlier studies all connections were initially assigned to a single group to give neurons access to all input signals at the beginning of training. We found out that such uniform solution not always is the best one. Thus within this text we compare efficiency of training of contextual neural networks with uniform and non-uniform, random initialization of connections groupings. On this basis we also discuss the properties of analyzed training algorithm which are related to characteristics of used scan-paths initialization methods.


Classification Self-consistency Scan-paths initialization Aggregation functions 


  1. 1.
    Huk, M.: Learning distributed selective attention strategies with the Sigma-if neural network. In: Akbar, M., Hussain, D. (eds.) Advances in Computer Science and IT, pp. 209–232. InTech, Vukovar (2009)Google Scholar
  2. 2.
    Huk, M.: Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network. Int. J. Appl. Math. Comput. Sci. 22, 449–459 (2012)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Huk, M.: Notes on the generalized backpropagation algorithm for contextual neural networks with conditional aggregation functions. J. Intell. Fuzzy Syst. 32, 1365–1376 (2017)CrossRefGoogle Scholar
  4. 4.
    Szczepanik, M., Jóźwiak, I.: Data management for fingerprint recognition algorithm based on characteristic points’ groups. In: Pechenizkiy, M., Wojciechowski, M. (eds.) New Trends in Databases and Information Systems. AISC, vol. 185, pp. 425–432. Springer, Heidelberg (2013). Scholar
  5. 5.
    Huk, M.: Measuring the effectiveness of hidden context usage by machine learning methods under conditions of increased entropy of noise. In: 2017 3rd IEEE International Conference on Cybernetics (CYBCONF), pp. 1–6. IEEE (2017)Google Scholar
  6. 6.
    Privitera, C.M., Azzariti, M., Stark, L.W.: Locating regions-of-interest for the Mars Rover expedition. Int. J. Remote Sens. 21, 3327–3347 (2000)CrossRefGoogle Scholar
  7. 7.
    Raczkowski, D., Canning, A.: Thomas-Fermi charge mixing for obtaining self-consistency in density functional calculations. Phys. Rev. B 64, 121101–121105 (2001)CrossRefGoogle Scholar
  8. 8.
    UCI Machine Learning Repository.
  9. 9.
    Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)CrossRefGoogle Scholar
  10. 10.
    Armstrong, S.A.: MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nat. Genet. 30, 41–47 (2002)CrossRefGoogle Scholar
  11. 11.
    Khan, J., et al.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med. 7(6), 673–679 (2001)CrossRefGoogle Scholar
  12. 12.
    Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63(2), 81–97 (1956)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Computer Science and ManagementWroclaw University of Science and TechnologyWroclawPoland

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