37 search results - page 1 / 8 » Homotopy-Based Semi-Supervised Hidden Markov Models for Sequ... |
COLING
2008
13 years 8 months ago
2008
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...
FLAIRS
2004
13 years 8 months ago
2004
Using unlabeled data to help supervised learning has become an increasingly attractive methodology and proven to be effective in many applications. This paper applies semi-supervi...
ICML
2001
IEEE
14 years 7 months ago
2001
IEEE
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hid...
BMCBI
2010
13 years 7 months ago
2010
Background: Existing hidden Markov model decoding algorithms do not focus on approximately identifying the sequence feature boundaries. Results: We give a set of algorithms to com...
ICML
2004
IEEE
14 years 11 days ago
2004
IEEE
We introduce a new perceptron-based discriminative learning algorithm for labeling structured data such as sequences, trees, and graphs. Since it is fully kernelized and uses poin...