1 research outputs found
String Measure Applied to String Self-Organizing Maps and Networks of Evolutionary Processors
* Supported by projects CCG08-UAM TIC-4425-2009 and TEC2007-68065-C03-02This 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