90 search results - page 2 / 18 » Maximum Entropy Markov Models for Information Extraction and... |
NIPS
2008
13 years 6 months ago
2008
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unatta...
ICML
2001
IEEE
14 years 5 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...
ICASSP
2010
IEEE
13 years 5 months ago
2010
IEEE
This paper addresses the problem of developing appropriate features for use in direct modeling approaches to speech recognition, such as those based on Maximum Entropy models or S...
KDD
2001
ACM
14 years 5 months ago
2001
ACM
Many of the same modeling methods used in natural languages, speci cally Markov models and HMM's, have also been applied to biological sequence analysis. In recent years, nat...
ICNC
2005
Springer
13 years 10 months ago
2005
Springer
This paper presents a novel texture segmentation method using Bayesian estimation and neural networks. Multi-scale wavelet coefficients and the context information extracted from n...