A New Model for Classifying DNA Code Inspired by Neural Networks and FSA

  • Byeong Kang
  • Andrei Kelarev
  • Arthur Sale
  • Ray Williams
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4303)


This paper introduces a new model of classifiers CL(V,E,ℓ,r) designed for classifying DNA sequences and combining the flexibility of neural networks and the generality of finite state automata. Our careful and thorough verification demonstrates that the classifiers CL(V,E,ℓ,r) are general enough and will be capable of solving all classification tasks for any given DNA dataset. We develop a minimisation algorithm for these classifiers and include several open questions which could benefit from contributions of various researchers throughout the world.


Neural Network Equivalence Class State Automaton Finite State Automaton Error Correct Output Code 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Byeong Kang
    • 1
  • Andrei Kelarev
    • 1
  • Arthur Sale
    • 1
  • Ray Williams
    • 1
  1. 1.School of ComputingUniversity of TasmaniaHobartAustralia

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