On the Projection of k-Valued Non-linearly Separable Problems into m-Valued Linearly Separable Problems View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011

AUTHORS

Igor Aizenberg

ABSTRACT

In this paper, we observe a new approach to learn non-linearly separable problems using a single multi-valued neuron. It is shown that a k-valued problem, which is non-linearly separable in the n-dimensional space can be projected into an m-valued (where m = kl) linearly separable problem in the same space. This projection can be utilized through a periodic activation function for the multi-valued neuron. Then the initial problem can be learned by a single multi-valued neuron using its learning algorithm. This approach is illustrated by the examples of such problems as XOR, Parity n, mod k addition of n variables and some benchmarks using a single multi-valued neuron. More... »

PAGES

223-235

Book

TITLE

Computational Intelligence

ISBN

978-3-642-20205-6
978-3-642-20206-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-20206-3_15

DOI

http://dx.doi.org/10.1007/978-3-642-20206-3_15

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1008150419


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