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» Feature Lattices for Maximum Entropy Modelling
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ICML
2003
IEEE
15 years 10 months ago
Hidden Markov Support Vector Machines
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines an...
Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofm...
116
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MLMI
2004
Springer
15 years 2 months ago
Shallow Dialogue Processing Using Machine Learning Algorithms (or Not)
This paper presents a shallow dialogue analysis model, aimed at human-human dialogues in the context of staff or business meetings. Four components of the model are defined, and ...
Andrei Popescu-Belis, Alexander Clark, Maria Georg...
CVPR
2003
IEEE
15 years 11 months ago
An Efficient Approach to Learning Inhomogeneous Gibbs Model
Inhomogeneous Gibbs model (IGM) [4] is an effective maximum entropy model in characterizing complex highdimensional distributions. However, its training process is so slow that th...
Ziqiang Liu, Hong Chen, Heung-Yeung Shum
ICPR
2010
IEEE
14 years 7 months ago
Exploiting Combined Multi-level Model for Document Sentiment Analysis
This paper focuses on the task of text sentiment analysis in hybrid online articles and web pages. Traditional approaches of text sentiment analysis typically work at a particular ...
Si Li, Hao Zhang, Weiran Xu, Guang Chen, Jun Guo
CVIU
2006
222views more  CVIU 2006»
14 years 9 months ago
Conditional models for contextual human motion recognition
We present algorithms for recognizing human motion in monocular video sequences, based on discriminative Conditional Random Field (CRF) and Maximum Entropy Markov Models (MEMM). E...
Cristian Sminchisescu, Atul Kanaujia, Dimitris N. ...