Abstract: This paper shows some ideas about how to incorporate a string learning stage in self-organizing algorithms. T. Kohonen and P. Somervuo have shown that self-organizing maps (SOM) are not restricted to numerical data. This paper proposes a symbolic measure that is used to implement a string self-organizing map based on SOM algorithm. Such measure between two strings is a new string. Computation over strings is performed using a priority relationship among symbols; in this case, symbolic measure is able to generate new symbols. A complementary operation is defined in order to apply such measure to DNA strands. Finally, an algorithm is proposed in order to be able to implement a string self-organizing map.

Keywords: Neural Network, Self-organizing Maps, and Control Feedback Methods.

ACM Classification Keywords: F.1.1 Models of Computation: Self-modifying machines (neural networks); F.1.2 Modes of Computation: Alternation and non-determinism.

Link:

STRING MEASURE APPLIED TO STRING SELF-ORGANIZING MAPS AND NETWORKS OF EVOLUTIONARY PROCESSORS1

Nuria Gómez Blas, Luis F. de Mingo, Francisco Gisbert, Juan M. Garitagoitia