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» Boosting for Model-Based Data Clustering
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ICIP
2008
IEEE
15 years 3 months ago
Learning efficient codes for 3D face recognition
Face representation based on the Visual Codebook becomes popular because of its excellent recognition performance, in which the critical problem is how to learn the most efficien...
Cheng Zhong, Zhenan Sun, Tieniu Tan
CVPR
2009
IEEE
16 years 4 months ago
Regularized Multi-Class Semi-Supervised Boosting
Many semi-supervised learning algorithms only deal with binary classification. Their extension to the multi-class problem is usually obtained by repeatedly solving a set of bina...
Amir Saffari, Christian Leistner, Horst Bischof
SDM
2004
SIAM
189views Data Mining» more  SDM 2004»
14 years 11 months ago
An Abstract Weighting Framework for Clustering Algorithms
act Weighting Framework for Clustering Algorithms Richard Nock Frank Nielsen Recent works in unsupervised learning have emphasized the need to understand a new trend in algorithmi...
Richard Nock, Frank Nielsen
84
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DEBU
2008
186views more  DEBU 2008»
14 years 9 months ago
A Survey of Collaborative Recommendation and the Robustness of Model-Based Algorithms
The open nature of collaborative recommender systems allows attackers who inject biased profile data to have a significant impact on the recommendations produced. Standard memory-...
Jeff J. Sandvig, Bamshad Mobasher, Robin D. Burke
IDA
2007
Springer
15 years 3 months ago
DENCLUE 2.0: Fast Clustering Based on Kernel Density Estimation
The Denclue algorithm employs a cluster model based on kernel density estimation. A cluster is defined by a local maximum of the estimated density function. Data points are assign...
Alexander Hinneburg, Hans-Henning Gabriel