Christos Ferles

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The self-organizing hidden Markov model map (SOHMMM) constitutes a cross-section between the theoretic foundations and algorithmic realizations of the self-organizing map (SOM) and the hidden Markov model (HMM). The intimate fusion and synergy of the SOM unsupervised training and HMM dynamic programming algorithms brings forth a novel on-line gradient(More)
A hybrid approach combining the Self-Organizing Map (SOM) and the Hidden Markov Model (HMM) is presented. The Self-Organizing Hidden Markov Model Map (SOHMMM) establishes a cross-section between the theoretic foundations and algorithmic realizations of its constituents. The respective architectures and learning methodologies are fused in an attempt to meet(More)
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