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Learning Process Behavior with EDY: an Experimental Analysis
Ugo Galassi
70 - 81
10.3233/978-1-58603-893-9-70
Frontiers in Artificial Intelligence and Applications
Volume 179: STAIRS 2008
Abstract

This paper presents an extensive evaluation, on artificial datasets, of EDY, an unsupervised algorithm for automatically synthesizing a Structured Hidden Markov Model (S-HMM) from a database of sequences. The goal of EDY is capturing the stochastic process by which the observed data was generated. The SHMM is a sub-class of Hidden Markov Model that exhibits a quasi-linear computational complexity and is well suited to real-time problems of process/user profiling. The datasets used for the evaluation are available on the web

http://www.edygroup.di.unipmn.it

. They are a proposal benchmark for the deep-testing and comparing of tools developed for analysis of temporal (spatial) sequences in which the objective is to reconstruct the generative model from which the sequences originated.

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