---
title: "Incremental Construction of Structured Hidden Markov Models"
authors:
  - "Ugo Galassi"
  - "Attilio Giordana"
  - "Lorenza Saitta"
doi: ""
url: "https://ijcai.org/proceedings/2007/128"
pdf: "https://ijcai.org/proceedings/2007/0128.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "798-802"
track: ""
keywords:
---

# Incremental Construction of Structured Hidden Markov Models

Ugo Galassi, Attilio Giordana, Lorenza Saitta

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 798-802.

## Abstract

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.