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» Using a Non-prior Training Active Feature Model
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IRAL
2003
ACM
15 years 2 months ago
Korean named entity recognition using HMM and CoTraining model
Namedentityrecognition isimportantinsophisticatedinformation service system such as Question Answering and Text Mining since most of the answer type and text mining unit depend on...
Euisok Chung, Yi-Gyu Hwang, Myung-Gil Jang
CISS
2008
IEEE
15 years 3 months ago
Unsupervised distributional anomaly detection for a self-diagnostic speech activity detector
— One feature that classification algorithms typically lack is the ability to know what they do not know. With this knowledge an algorithm would be able to operate in any domain...
Nash M. Borges, Gerard G. L. Meyer
EMNLP
2008
14 years 11 months ago
Online Large-Margin Training of Syntactic and Structural Translation Features
Minimum-error-rate training (MERT) is a bottleneck for current development in statistical machine translation because it is limited in the number of weights it can reliably optimi...
David Chiang, Yuval Marton, Philip Resnik
TIP
2008
165views more  TIP 2008»
14 years 9 months ago
Activity Modeling Using Event Probability Sequences
Changes in motion properties of trajectories provide useful cues for modeling and recognizing human activities. We associate an event with significant changes that are localized in...
Naresh P. Cuntoor, B. Yegnanarayana, Rama Chellapp...
ICCV
2003
IEEE
15 years 11 months ago
Conditional Feature Sensitivity: A Unifying View on Active Recognition and Feature Selection
The objective of active recognition is to iteratively collect the next "best" measurements (e.g., camera angles or viewpoints), to maximally reduce ambiguities in recogn...
Xiang Sean Zhou, Dorin Comaniciu, Arun Krishnan