Sciweavers

Share
IJCAI
2007

Incremental Construction of Structured Hidden Markov Models

8 years 7 months ago
Incremental Construction of Structured Hidden Markov Models
This paper presents an algorithm for inferring a Structured Hidden Markov Model (S-HMM) from a set of sequences. The S-HMMs are a sub-class of the Hierarchical Hidden Markov Models and are well suited to problems of process/user profiling. The learning algorithm is unsupervised, and follows a mixed bottom-up/top-down strategy, in which elementary facts in the sequences (motifs) are progressively grouped, thus building up the abstraction hierarchy of a S-HMM, layer after layer. The algorithm is validated on a suite of artificial datasets, where the challenge for the learning algorithm is to reconstruct the model that generated the data. Then, an application to a real problem of molecular biology is briefly described.
Ugo Galassi, Attilio Giordana, Lorenza Saitta
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where IJCAI
Authors Ugo Galassi, Attilio Giordana, Lorenza Saitta
Comments (0)
books