Cooperative Growing Hierarchical Recurrent Self Organizing Model for Phoneme Recognition
نویسنده
چکیده
Among the large number of research publications discussing the SOM (Self-Organizing Map) [1, 2, 18, 19] different variants and extensions have been introduced. One of the SOM based models is the Growing Hierarchical Self-Organizing Map (GHSOM) [3-6]. The GHSOM is a neural architecture combining the advantages of two principal extensions of the self-organizing map, dynamic growth and hierarchical structure. Basically, this neural network model is composed of independent SOMs (many SOM), each of which is allowed to grow in size during the training process until a certain quality criterion regarding data representation is met. Consequently, the structure of this adaptive architecture automatically adapts itself according to the structure of the input space during the training process.
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