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ICML
2000
IEEE
15 years 10 months ago
Maximum Entropy Markov Models for Information Extraction and Segmentation
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...
AAAI
2000
14 years 11 months ago
Information Extraction with HMM Structures Learned by Stochastic Optimization
Recent research has demonstrated the strong performance of hidden Markov models applied to information extraction--the task of populating database slots with corresponding phrases...
Dayne Freitag, Andrew McCallum
ICPR
2006
IEEE
15 years 11 months ago
Hidden Markov Models for Optical Flow Analysis in Crowds
This paper presents an event detector for emergencies in crowds. Assuming a single camera and a dense crowd we rely on optical flow instead of tracking statistics as a feature to ...
Ernesto L. Andrade, Scott Blunsden, Robert B. Fish...
COLING
2008
14 years 11 months ago
Homotopy-Based Semi-Supervised Hidden Markov Models for Sequence Labeling
This paper explores the use of the homotopy method for training a semi-supervised Hidden Markov Model (HMM) used for sequence labeling. We provide a novel polynomial-time algorith...
Gholamreza Haffari, Anoop Sarkar
BMCBI
2005
141views more  BMCBI 2005»
14 years 9 months ago
A method for the prediction of GPCRs coupling specificity to G-proteins using refined profile Hidden Markov Models
Background: G- Protein coupled receptors (GPCRs) comprise the largest group of eukaryotic cell surface receptors with great pharmacological interest. A broad range of native ligan...
Nikolaos G. Sgourakis, Pantelis G. Bagos, Panagiot...