Corpus ID: 598018

Some Competitive Learning Methods Contents 1 Introduction 3 2 Common Properties & Notational Conventions 4 3 Goals of Competitive Learning 7

  title={Some Competitive Learning Methods Contents 1 Introduction 3 2 Common Properties \& Notational Conventions 4 3 Goals of Competitive Learning 7},
  author={Bernd Fritzke},
(Some additions and reenements are planned for this document so it will stay in the draft status still for a while.) Comments are welcome. Abstract This report has the purpose of describing several algorithms from the literature all related to competitive learning. A uniform terminology is used for all methods. Moreover, identical examples are provided to allow a qualitative comparisons of the methods. The on-line version 1 of this document contains hyperlinks to Java implementations of several… Expand
A Mixed Ensemble Approach for the Semi-supervised Problem
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  • S. A. Shehabi, J. Lamirel
  • Computer Science
  • Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.
  • 2005
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Fast adaptive k-means clustering: some empirical results
  • C. Darken, J. Moody
  • Computer Science
  • 1990 IJCNN International Joint Conference on Neural Networks
  • 1990
The authors present learning rate schedules for fast adaptive k-means clustering which surpass the standard MacQueen learning rate schedule (J. MacQeen, 1967) in speed and quality of solution byExpand
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This competitive Hebbian rule provides a novel approach to the problem of constructing topology preserving feature maps and representing intricately structured manifolds and makes this novel approach particularly useful in all applications where neighborhood relations have to be exploited or the shape and topology of submanifolds have to been take into account. Expand
Improving the Learning Speed in Topological Maps of Patterns
A method of improving the learning speed, by starting the map with very few units and increasing that number progressively until the map reaches its final size, which dramatically reduces the time needed for the “unfolding" phase and also yields some improvements in the asymptotic convergence phase. Expand
Adding Learned Expectation Into the Learning Procedure of Self-Organizing Maps
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  • Computer Science
  • Int. J. Neural Syst.
  • 1990
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  • Duane DeSieno
  • Computer Science
  • IEEE 1988 International Conference on Neural Networks
  • 1988
The author introduces a modification of Kohonen learning that provides rapid convergence and improved representation of the input data and forms a better approximation of p(x) in many areas of pattern recognition, statistical analysis, and control. Expand
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