Learning Process Behavior with EDY: an Experimental Analysis

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.

• Type: Conference
• Year: 2008
• Where: STAIRS
• Authors: Ugo Galassi
• Keywords: Education, Hidden Markov Model, Quasi-linear Computational Complexity, STAIRS 2008, Structured Hidden Markov