Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4942136 | Artificial Intelligence | 2017 | 59 Pages |
Abstract
We present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of linear-chain conditional random fields to model deep nested Markov processes. It is parameterised as a conditional log-linear model and has polynomial time algorithms for learning and inference. We derive algorithms for partially-supervised learning and constrained inference. We develop numerical scaling procedures that handle the overflow problem. We show that when depth is two, the HSCRF can be reduced to the semi-Markov conditional random fields. Finally, we demonstrate the HSCRF on two applications: (i) recognising human activities of daily living (ADLs) from indoor surveillance cameras, and (ii) noun-phrase chunking. The HSCRF is capable of learning rich hierarchical models with reasonable accuracy in both fully and partially observed data cases.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Truyen Tran, Dinh Phung, Hung Bui, Svetha Venkatesh,