Learning Complex and Sparse Events in Long Sequences

The Hierarchical Hidden Markov Model (HHMM) is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. Even if effective algorithms are available to estimate HHMM parameters from sequences, little has been done in order to automatize the construction of the model architecture. The primary focus of this paper is on a multi-strategy algorithm for inferring the HHMM structure from a set of sequences, where the events to capture are present in a relevant portion of them. The algorithm follows a bottom-up strategy, in which elementary facts in the s are progressively grouped, thus building the abstraction hierarchy of a HHMM, layer after layer. In this process, clustering algorithms and sequence alignment algorithms, widely used in domains like molecular biology, are exploited. The induction strategy has been designed in order to deal with events characterized by a sparse structure, where gaps filled by irrelevant facts can be intermixed with the re...

• Type: Conference
• Year: 2004
• Where: ECAI
• Authors: Marco Botta, Ugo Galassi, Attilio Giordana
• Keywords: Algorithms, Artificial Intelligence, ECAI 2004, Hierarchical Hidden Markov Model, Irrelevant Facts