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» Incremental Mixture Learning for Clustering Discrete Data
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ICML
2009
IEEE
14 years 8 months ago
Archipelago: nonparametric Bayesian semi-supervised learning
Semi-supervised learning (SSL), is classification where additional unlabeled data can be used to improve accuracy. Generative approaches are appealing in this situation, as a mode...
Ryan Prescott Adams, Zoubin Ghahramani
CVPR
2012
IEEE
11 years 10 months ago
Non-negative low rank and sparse graph for semi-supervised learning
Constructing a good graph to represent data structures is critical for many important machine learning tasks such as clustering and classification. This paper proposes a novel no...
Liansheng Zhuang, Haoyuan Gao, Zhouchen Lin, Yi Ma...
APIN
2004
131views more  APIN 2004»
13 years 7 months ago
Rough Self Organizing Map
A rough self-organizing map (RSOM) with fuzzy discretization of feature space is described here. Discernibility reducts obtained using rough set theory are used to extract domain k...
Sankar K. Pal, Biswarup Dasgupta, Pabitra Mitra
GI
2007
Springer
14 years 1 months ago
Background Modeling Using Adaptive Cluster Density Estimation for Automatic Human Detection
: Detection is an inherent part of every advanced automatic tracking system. In this work we focus on automatic detection of humans by enhanced background subtraction. Background s...
Harish Bhaskar, Lyudmila Mihaylova, Simon Maskell
BMCBI
2008
134views more  BMCBI 2008»
13 years 7 months ago
Clustering cancer gene expression data: a comparative study
Background The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have propose...
Marcílio Carlos Pereira de Souto, Ivan G. C...