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» Learning Generative Models with the Up-Propagation Algorithm
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FSS
2010
147views more  FSS 2010»
13 years 6 months ago
A divide and conquer method for learning large Fuzzy Cognitive Maps
Fuzzy Cognitive Maps (FCMs) are a convenient tool for modeling and simulating dynamic systems. FCMs were applied in a large number of diverse areas and have already gained momentu...
Wojciech Stach, Lukasz A. Kurgan, Witold Pedrycz
ICIP
2009
IEEE
13 years 5 months ago
Object tracking by bidirectional learning with feature selection
This paper proposes a new tracking algorithm which combines object and background information, via building object and background appearance models simultaneously by nonparametric...
Heng Wang, Xinwen Hou, Cheng-Lin Liu
WWW
2008
ACM
14 years 8 months ago
Using the wisdom of the crowds for keyword generation
In the sponsored search model, search engines are paid by businesses that are interested in displaying ads for their site alongside the search results. Businesses bid for keywords...
Ariel Fuxman, Panayiotis Tsaparas, Kannan Achan, R...
NN
2008
Springer
13 years 7 months ago
Multilayer in-place learning networks for modeling functional layers in the laminar cortex
Currently, there is a lack of general-purpose in-place learning networks that model feature layers in the cortex. By "general-purpose" we mean a general yet adaptive hig...
Juyang Weng, Tianyu Luwang, Hong Lu, Xiangyang Xue
IJCV
2008
167views more  IJCV 2008»
13 years 7 months ago
Learning Layered Motion Segmentations of Video
We present an unsupervised approach for learning a generative layered representation of a scene from a video for motion segmentation. The learnt model is a composition of layers, ...
M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserma...