Some of the usual problems for Learning vector quantization (LVQ) based methods are that one cannot optimally guess about the number of prototypes required for initialization for multimodal data structures i.e.these algorithms are very sensitive to initialization of prototypes and one has to pre define the optimal number of prototypes before running the algorithm. If a prototype, for some reasons, is ‘outside’ the cluster which it should represent and if there are points of a different categories in between, then the other points act as a barrier and the prototype will not find its optimum position during training. Since the model complexity is not known in many cases, we avoid this problem by introducing a "Dynamic" version of LVQ.

Dynamic-GRLVQ (DGRLVQ), which adapts the model complexity to the given problem during training by adding or removing prototypes dynamically/realtime one by one for each category until satisfactory classification results are achieved.

Features

  • Dynamic generalization relevance learning vector quantization
  • DGRLVQ
  • LVQ
  • GRLVQ
  • Machine Learning
  • Clustering
  • Artificial intelligence
  • classification
  • Pattern Recognition

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License

GNU Library or Lesser General Public License version 3.0 (LGPLv3)

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Additional Project Details

Operating Systems

Linux, Android, Apple iPhone, Mac, Windows

Intended Audience

Information Technology, Science/Research

User Interface

Java Swing

Programming Language

Java

Related Categories

Java Artificial Intelligence Software, Java Machine Learning Software

Registered

2018-04-03