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» Language model adaptation using Random Forests
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EMNLP
2009
13 years 5 months ago
Natural Language Generation with Tree Conditional Random Fields
This paper presents an effective method for generating natural language sentences from their underlying meaning representations. The method is built on top of a hybrid tree repres...
Wei Lu, Hwee Tou Ng, Wee Sun Lee
CAISE
2003
Springer
14 years 22 days ago
Modelling Telecare Service Requirements for Older People Using the Unified Modelling Language
Providing technology support for older people offers distinct challenges for social and IT systems delivery. The definition and integration of services, the diversity of supply, va...
Ken Lunn, Andrew Sixsmith, Ann Lindsay, Marja Vaar...
ICASSP
2011
IEEE
12 years 11 months ago
Deep Belief Networks using discriminative features for phone recognition
Deep Belief Networks (DBNs) are multi-layer generative models. They can be trained to model windows of coefficients extracted from speech and they discover multiple layers of fea...
Abdel-rahman Mohamed, Tara N. Sainath, George Dahl...
BMCBI
2010
190views more  BMCBI 2010»
13 years 7 months ago
Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification alg
Background: Data generated using `omics' technologies are characterized by high dimensionality, where the number of features measured per subject vastly exceeds the number of...
Yu Guo, Armin Graber, Robert N. McBurney, Raji Bal...

Publication
335views
11 years 9 months ago
Person Re-Identification: What Features are Important?
State-of-the-art person re-identi cation methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with a single ve...
Chunxiao Liu, Shaogang Gong, Chen Change Loy, Xing...