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» Learning the structure of manifolds using random projections
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ICML
2005
IEEE
16 years 19 days ago
Predicting protein folds with structural repeats using a chain graph model
Protein fold recognition is a key step towards inferring the tertiary structures from amino-acid sequences. Complex folds such as those consisting of interacting structural repeat...
Yan Liu, Eric P. Xing, Jaime G. Carbonell
SODA
2012
ACM
268views Algorithms» more  SODA 2012»
13 years 2 months ago
Analyzing graph structure via linear measurements
We initiate the study of graph sketching, i.e., algorithms that use a limited number of linear measurements of a graph to determine the properties of the graph. While a graph on n...
Kook Jin Ahn, Sudipto Guha, Andrew McGregor
ICCV
2007
IEEE
16 years 1 months ago
Steerable Random Fields
In contrast to traditional Markov random field (MRF) models, we develop a Steerable Random Field (SRF) in which the field potentials are defined in terms of filter responses that ...
Stefan Roth, Michael J. Black
COBUILD
1998
Springer
15 years 4 months ago
The Metaphor of Virtual Rooms in the Cooperative Learning Environment CLear
In the CLear project we develop a cooperative learning system for supporting learning and training processes of co-located and distributed groups. One of the fundamental concepts o...
Hans-Rüdiger Pfister, Christian Schuckmann, J...
ICMCS
2009
IEEE
415views Multimedia» more  ICMCS 2009»
14 years 9 months ago
A new localized superpixel Markov random field for image segmentation
In this paper, we present a novel localized Markov random field (MRF) method based on superpixels for region segmentation. Early vision problems could be formulated as pixel label...
Xiaofeng Wang, Xiao-Ping Zhang