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Cascade-Correlation

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  • Baluja S, Fahlman SE (1994) Reducing network depth in the cascade-correlation learning architecture. School of Computer Science, Carnegie Mellon University, Pittsburgh

    Google Scholar 

  • Buckingham D, Shultz TR (2000) The developmental course of distance, time, and velocity concepts: a generative connectionist model. J Cognit Dev 1:305–345

    Article  Google Scholar 

  • Dandurand F, Berthiaume V, Shultz TR (2007) A systematic comparison of flat and standard cascade-correlation using a student-teacher network approximation task. Connect Sci 19:223–244

    Article  Google Scholar 

  • Fahlman SE (1988) Faster-learning variations on back-propagation: an empirical study. In: Touretzky DS, Hinton GE, Sejnowski TJ (eds) Proceedings of the 1988 connectionist models summer school. Morgan Kaufmann, Los Altos, pp 38–51

    Google Scholar 

  • Fahlman SE (1991) The recurrent cascade-correlation architecture. In: Touretzky DS (ed) Advances in neural information processing systems, vol 3. Morgan Kaufmann, Los Altos

    Google Scholar 

  • Fahlman SE, Lebiere C (1990) The cascade-correlation learning architecture. In: Touretzky DS (ed) Advances in neural information processing systems, vol 2. Morgan Kaufmann, Los Altos, pp 524–532

    Google Scholar 

  • Mareschal D, Shultz TR (1999) Development of children’s seriation: a connectionist approach. Connect Sci 11:149–186

    Article  Google Scholar 

  • Oshima-Takane Y, Takane Y, Shultz TR (1999) The learning of first and second pronouns in English: network models and analysis. J Child Lang 26:545–575

    Article  Google Scholar 

  • Schlimm D, Shultz TR (2009) Learning the structure of abstract groups. In: Taatgen NA, Rijn HV (eds) Proceedings of the 31st annual conference of the cognitive science society. Cognitive Science Society, Austin, pp 2950–2955

    Google Scholar 

  • Shultz TR (1998) A computational analysis of conservation. Dev Sci 1:103–126

    Article  Google Scholar 

  • Shultz TR (2003) Computational developmental psychology. MIT, Cambridge

    Google Scholar 

  • Shultz TR (2006) Constructive learning in the modeling of psychological development. In: Munakata Y, Johnson MH (eds) Processes of change in brain and cognitive development: attention and performance XXI. Oxford University Press, Oxford, pp 61–86

    Google Scholar 

  • Shultz TR, Bale AC (2006) Neural networks discover a near-identity relation to distinguish simple syntactic forms. Minds Mach 16:107–139

    Article  Google Scholar 

  • Shultz TR, Cohen LB (2004) Modeling age differences in infant category learning. Infancy 5:153–171

    Article  Google Scholar 

  • Shultz TR, Mareschal D, Schmidt WC (1994) Modeling cognitive development on balance scale phenomena. Mach Learn 16:57–86

    Google Scholar 

  • Shultz TR, Rivest F (2001) Knowledge-based cascade-correlation: using knowledge to speed learning. Connect Sci 13:1–30

    Article  Google Scholar 

  • Shultz TR, Rivest F, Egri L, Thivierge J-P, Dandurand F (2007) Could knowledge-based neural learning be useful in developmental robotics? The case of KBCC. Int J Humanoid Robot 4:245–279

    Article  Google Scholar 

  • Shultz TR, Takane Y (2007) Rule following and rule use in simulations of the balance-scale task. Cognition 103:460–472

    Article  Google Scholar 

  • Shultz TR, Vogel A (2004) A connectionist model of the development of transitivity. In: Proceedings of the twenty-sixth annual conference of the cognitive science society. Erlbaum, Mahwah, pp 1243–1248

    Google Scholar 

  • Sirois S, Shultz TR (1998) Neural network modeling of developmental effects in discrimination shifts. J Exp Child Psychol 71:235–274

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. McGill University, Montréal, QC, Canada

    Thomas R.Shultz

  2. Carnegie Mellon University, Pittsburgh, PA, USA

    Scott E.Fahlman

Authors
  1. Thomas R.Shultz
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  2. Scott E.Fahlman
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Corresponding author

Correspondence to Thomas R.Shultz .

Editor information

Editors and Affiliations

  1. Engineering (CSE), University of New South Wales School of Computer Science &, Sydney, New South Wales, Australia

    Claude Sammut

  2. Software Engineering, Monash University School of Computer Science &, Melbourne, Victoria, Australia

    Geoffrey I. Webb

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R.Shultz, T., E.Fahlman, S. (2014). Cascade-Correlation. In: Sammut, C., Webb, G. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7502-7_33-1

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  • DOI: https://doi.org/10.1007/978-1-4899-7502-7_33-1

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  • Publisher Name: Springer, Boston, MA

  • Online ISBN: 978-1-4899-7502-7

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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Chapter history

  1. Latest

    Cascade–Correlation
    Published:
    27 April 2022

    DOI: https://doi.org/10.1007/978-1-4899-7502-7_33-2

  2. Original

    Cascade-Correlation
    Published:
    17 February 2015

    DOI: https://doi.org/10.1007/978-1-4899-7502-7_33-1

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