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ICPR
2008
IEEE
15 years 10 months ago
A clustering algorithm combine the FCM algorithm with supervised learning normal mixture model
In this paper we propose a new clustering algorithm which combines the FCM clustering algorithm with the supervised learning normal mixture model; we call the algorithm as the FCM...
Wei Wang, Chunheng Wang, Xia Cui, Ai Wang
NPL
2000
95views more  NPL 2000»
15 years 3 months ago
Bayesian Sampling and Ensemble Learning in Generative Topographic Mapping
Generative topographic mapping (GTM) is a statistical model to extract a hidden smooth manifold from data, like the self-organizing map (SOM). Although a deterministic search algo...
Akio Utsugi
ICCV
2001
IEEE
16 years 6 months ago
Separating Appearance from Deformation
By representing images and image prototypes by linear subspaces spanned by "tangent vectors" (derivatives of an image with respect to translation, rotation, etc.), impre...
Nebojsa Jojic, Patrice Simard, Brendan J. Frey, Da...
JMLR
2010
172views more  JMLR 2010»
14 years 11 months ago
Modeling annotator expertise: Learning when everybody knows a bit of something
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this p...
Yan Yan, Rómer Rosales, Glenn Fung, Mark W....
JUCS
2008
119views more  JUCS 2008»
15 years 4 months ago
A Standards-based Modelling Approach for Dynamic Generation of Adaptive Learning Scenarios
: One of the key problems in developing standard based adaptive courses is the complexity involved in the design phase, especially when establishing the hooks for the dynamic model...
Jesus Boticario, Olga C. Santos