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Related Experiment Videos

Encoded pattern classification using constructive learning algorithms based on learning vector quantization.

C N Ganesh Murthy1, Y V Venkatesh

  • 1Computer Vision and Artificial Intelligence Laboratory, Department of Electrical Engineering, Indian Institute of Science, Bangalore-560012, India.

Neural Networks : the Official Journal of the International Neural Network Society
|March 29, 2003
PubMed
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A novel encoding technique and training methods for artificial neural networks (ANNs) improve pattern recognition accuracy and efficiency. These methods are robust for classifying noisy and distorted patterns.

Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Pattern Recognition

Background:

  • Artificial neural networks (ANNs) are widely used for pattern recognition.
  • Kohonen-type networks offer unsupervised learning capabilities.
  • Efficient training and encoding are crucial for ANN performance.

Purpose of the Study:

  • To propose a novel encoding technique for pattern recognition using Kohonen-type ANNs.
  • To evaluate four different training techniques for ANNs, including Kohonen's self-organizing network and learning vector quantization (LVQ).
  • To introduce two constructive learning algorithms, MLVQ and TLVQ, to enhance network performance and minimize size.

Main Methods:

  • A radial grid overlay and conversion to a 2D feature array for training input.

Related Experiment Videos

  • Training ANNs using Kohonen's self-organizing network, LVQ, MLVQ, and TLVQ algorithms.
  • Classifying patterns by comparing input to trained neuron labels based on proximity.

    • Main Results:

    • The encoding strategy effectively clusters distorted versions of the same pattern.
    • Kohonen's self-organizing network assigns labels to neurons, enabling pattern classification.
    • LVQ, MLVQ, and TLVQ algorithms demonstrate efficiency and robustness in classifying noiseless and noisy patterns.

    Conclusions:

    • The proposed pattern encoding strategy is effective for Kohonen-type ANNs.
    • The developed training techniques, particularly MLVQ and TLVQ, improve recognition accuracy and network efficiency.
    • The methods are robust for handling noisy and distorted pattern recognition tasks.