A Comparison between Artificial Neural Network and Cascade-Correlation Neural Network in Concept Classification

  • Yanming Guo
  • Liang Bai
  • Songyang Lao
  • Song Wu
  • Michael S. Lew
Conference paper

DOI: 10.1007/978-3-319-13168-9_26

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8879)
Cite this paper as:
Guo Y., Bai L., Lao S., Wu S., Lew M.S. (2014) A Comparison between Artificial Neural Network and Cascade-Correlation Neural Network in Concept Classification. In: Ooi W.T., Snoek C.G.M., Tan H.K., Ho CK., Huet B., Ngo CW. (eds) Advances in Multimedia Information Processing – PCM 2014. PCM 2014. Lecture Notes in Computer Science, vol 8879. Springer, Cham

Abstract

Deep learning has achieved significant attention recently due to promising results in representing and classifying concepts most prominently in the form of convolutional neural networks (CNN). While CNN has been widely studied and evaluated in computer vision, there are other forms of deep learning algorithms which may be promising. One interesting deep learning approach which has received relatively little attention in visual concept classification is Cascade-Correlation Neural Networks (CCNN). In this paper, we create a visual concept retrieval system which is based on CCNN. Experimental results on the CalTech101 dataset indicate that CCNN outperforms ANN.

Keywords

artificial neural network cascade-correlation neural network deep learning concept classification 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yanming Guo
    • 1
    • 2
  • Liang Bai
    • 2
  • Songyang Lao
    • 2
  • Song Wu
    • 1
  • Michael S. Lew
    • 1
  1. 1.LIACS Media LabLeiden UniversityLeidenThe Netherlands
  2. 2.College of Information Systems and ManagementNational University of Defense TechnologyChangshaChina

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