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ICWSM
2009
8 years 12 months ago
A Categorical Model for Discovering Latent Structure in Social Annotations
The advent of social tagging systems has enabled a new community-based view of the Web in which objects like images, videos, and Web pages are annotated by thousands of users. Und...
Said Kashoob, James Caverlee, Ying Ding
CLEF
2009
Springer
9 years 3 months ago
Unsupervised Word Decomposition with the Promodes Algorithm
We present Promodes, an algorithm for unsupervised word decomposition, which is based on a probabilistic generative model. The model considers segment boundaries as hidden variable...
Sebastian Spiegler, Bruno Golénia, Peter A....
NIPS
2007
9 years 3 months ago
Learning Visual Attributes
We present a probabilistic generative model of visual attributes, together with an efficient learning algorithm. Attributes are visual qualities of objects, such as ‘red’, ...
Vittorio Ferrari, Andrew Zisserman
EMNLP
2007
9 years 3 months ago
Bayesian Document Generative Model with Explicit Multiple Topics
In this paper, we proposed a novel probabilistic generative model to deal with explicit multiple-topic documents: Parametric Dirichlet Mixture Model(PDMM). PDMM is an expansion of...
Issei Sato, Hiroshi Nakagawa
CVPR
2010
IEEE
9 years 10 months ago
Clustering Dynamic Textures with the Hierarchical EM Algorithm
The dynamic texture (DT) is a probabilistic generative model, defined over space and time, that represents a video as the output of a linear dynamical system (LDS). The DT model ...
Antoni Chan, Emanuele Coviello, Gert Lanckriet
KDD
2007
ACM
237views Data Mining» more  KDD 2007»
10 years 2 months ago
Knowledge discovery of multiple-topic document using parametric mixture model with dirichlet prior
Documents, such as those seen on Wikipedia and Folksonomy, have tended to be assigned with multiple topics as a meta-data. Therefore, it is more and more important to analyze a re...
Issei Sato, Hiroshi Nakagawa
KDD
2008
ACM
257views Data Mining» more  KDD 2008»
10 years 2 months ago
Knowledge discovery of semantic relationships between words using nonparametric bayesian graph model
We developed a model based on nonparametric Bayesian modeling for automatic discovery of semantic relationships between words taken from a corpus. It is aimed at discovering seman...
Issei Sato, Minoru Yoshida, Hiroshi Nakagawa
ICCV
2007
IEEE
10 years 4 months ago
Non-Parametric Probabilistic Image Segmentation
We propose a simple probabilistic generative model for image segmentation. Like other probabilistic algorithms (such as EM on a Mixture of Gaussians) the proposed model is princip...
Marco Andreetto, Lihi Zelnik-Manor, Pietro Perona
CVPR
2008
IEEE
10 years 4 months ago
Simultaneous clustering and tracking unknown number of objects
In this paper, we present a novel on-line probabilistic generative model that simultaneously deals with both the clustering and the tracking of an unknown number of moving objects...
Katsuhiko Ishiguro, Takeshi Yamada, Naonori Ueda
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