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» Estimating Likelihoods for Topic Models
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KDD
2010
ACM
435views Data Mining» more  KDD 2010»
15 years 4 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
118
Voted
ECIR
2010
Springer
15 years 2 months ago
Tripartite Hidden Topic Models for Personalised Tag Suggestion
Abstract. Social tagging systems provide methods for users to categorise resources using their own choice of keywords (or "tags") without being bound to a restrictive set...
Morgan Harvey, Mark Baillie, Ian Ruthven, Mark Jam...
CVPR
2000
IEEE
16 years 2 months ago
Learning in Gibbsian Fields: How Accurate and How Fast Can It Be?
?Gibbsian fields or Markov random fields are widely used in Bayesian image analysis, but learning Gibbs models is computationally expensive. The computational complexity is pronoun...
Song Chun Zhu, Xiuwen Liu
105
Voted
ACL
2006
15 years 2 months ago
An All-Subtrees Approach to Unsupervised Parsing
We investigate generalizations of the allsubtrees "DOP" approach to unsupervised parsing. Unsupervised DOP models assign all possible binary trees to a set of sentences ...
Rens Bod
90
Voted
ACCV
2010
Springer
14 years 8 months ago
Human Pose Estimation Using Exemplars and Part Based Refinement
In this paper, we proposed a fast and accurate human pose estimation framework that combines top-down and bottom-up methods. The framework consists of an initialization stage and a...
Yanchao Su, Haizhou Ai, Takayoshi Yamashita, Shiho...