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Optimal Spherical Separability: Artificial Neural Networks

  • Rama Murthy GarimellaEmail author
  • Ganesh Yaparla
  • Rhishi Pratap Singh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10305)

Abstract

In this research paper, the concept of hyper-spherical/hyper-ellipsoidal separability is introduced. Method of arriving at the optimal hypersphere (maximizing margin) separating two classes is discussed. By projecting the quantized patterns into higher dimensional space (as in encoders of error correcting code), the patterns are made hyper-spherically separable. Single/multiple layers of spherical/ellipsoidal neurons are proposed for multi-class classification. An associative memory based on hyper-ellipsoidal neuron is proposed.

Keywords

Associative Memory High Dimensional Space Code Word Linear Separability Pattern Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Rama Murthy Garimella
    • 1
    Email author
  • Ganesh Yaparla
    • 1
  • Rhishi Pratap Singh
    • 1
  1. 1.International Institute of Information TechnologyHyderabadIndia

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