Automatic Classification of Volcanic Earthquakes in HMM-Induced Vector Spaces
Even though hidden Markov models (HMMs) have been used for the automatic classification of volcanic earthquakes, their usage has been so far limited to the Bayesian scheme. Recently proposed alternatives, proven in other application scenarios, consist in building HMM-induced vector spaces where discriminative classification techniques can be applied. In this paper, a simple vector space is induced by considering log-likelihoods of the HMMs (per-class) as dimensions. Experimental results show that the discriminative classification in such an induced space leads to better performances than those obtained with the standard Bayesian scheme.
KeywordsAutomatic classification generative embedding hidden Markov models model-induced feature space seismic-volcanic signals
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