We study a stock trading method based on dynamic bayesian networks to model the dynamics of the trend of stock prices. We design a three level hierarchical hidden Markov model (HHM...
Jangmin O, Jae Won Lee, Sung-Bae Park, Byoung-Tak ...
We propose mixtures of hidden Markov models for modelling clickstreams of web surfers. Hence, the page categorization is learned from the data without the need for a (possibly cumb...
Automatic extraction of content description from commercial audio recordings has a number of important applications, from indexing and retrieval through to novel musicological ana...
In this work, we introduce an Interactive Parts (IP) model as an alternative to Hidden Markov Models (HMMs). We tested both models on a database of on-line cursive script. We show...
Structural information about a document is essential for structured query processing, indexing, and retrieval. A document page can be partitioned into a hierarchy of homogeneous r...