Abstract
This paper presents a new variant of a well known competitive learning algorithm: Growing Hierarchical Recurrent Self Organizing Map (GH_RSOM). The proposed variant is like the basic Growing Hierarchical Self Organizing Map (GHSOM), however, in the GH_RSOM each map of each layer is a recurrent SOM (RSOM) it is characterized for each unit of the map by a difference vector which is used for selecting the best matching unit and also for adaptation of weights of the map. In this paper, we study the learning quality of the proposed GHSOM variant and we show that it is able to reach good vowels recognition rates.
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Jlassi, C., Arous, N., Ellouze, N. (2010). The Growing Hierarchical Recurrent Self Organizing Map for Phoneme Recognition. In: Solé-Casals, J., Zaiats, V. (eds) Advances in Nonlinear Speech Processing. NOLISP 2009. Lecture Notes in Computer Science(), vol 5933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11509-7_24
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DOI: https://doi.org/10.1007/978-3-642-11509-7_24
Publisher Name: Springer, Berlin, Heidelberg
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