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» Extractive summarization using a latent variable model
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GRC
2010
IEEE
13 years 7 months ago
Learning Multiple Latent Variables with Self-Organizing Maps
Inference of latent variables from complicated data is one important problem in data mining. The high dimensionality and high complexity of real world data often make accurate infe...
Lili Zhang, Erzsébet Merényi
KDD
2010
ACM
435views Data Mining» more  KDD 2010»
13 years 10 months ago
Topic models with power-law using Pitman-Yor process
One of the important approaches for Knowledge discovery and Data mining is to estimate unobserved variables because latent variables can indicate hidden and specific properties o...
Issei Sato, Hiroshi Nakagawa
ICASSP
2011
IEEE
12 years 10 months ago
Concept-based classification for multi-document summarization
Documents often contain inherently many concepts reflecting specific and generic aspects. To automatically generate a short summary text of documents on similar topics, it is im...
Asli Çelikyilmaz, Dilek Hakkani-Tür
HUMO
2007
Springer
14 years 17 days ago
Modeling Human Locomotion with Topologically Constrained Latent Variable Models
Abstract. Learned, activity-specific motion models are useful for human pose and motion estimation. Nevertheless, while the use of activityspecific models simplifies monocular t...
Raquel Urtasun, David J. Fleet, Neil D. Lawrence
ICML
2005
IEEE
14 years 7 months ago
New d-separation identification results for learning continuous latent variable models
Learning the structure of graphical models is an important task, but one of considerable difficulty when latent variables are involved. Because conditional independences using hid...
Ricardo Silva, Richard Scheines