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ICIP
2007
IEEE
15 years 6 months ago
Unsupervised Nonlinear Manifold Learning
This communication deals with data reduction and regression. A set of high dimensional data (e.g., images) usually has only a few degrees of freedom with corresponding variables t...
Matthieu Brucher, Christian Heinrich, Fabrice Heit...
CSSE
2008
IEEE
15 years 4 months ago
JThreadSpy: A Tool for Improving the Effectiveness of Concurrent System Teaching and Learning
Both teaching and learning multithreaded ing are complex tasks, due to the abstraction of the concepts, the non-determinism of the scheduler, the impossibility of using classical s...
Giovanni Malnati, Caterina Maria Cuva, Claudia Bar...
UAI
1996
15 years 3 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
NIPS
1990
15 years 3 months ago
Learning to See Rotation and Dilation with a Hebb Rule
Previous work (M.I. Sereno, 1989; cf. M.E. Sereno, 1987) showed that a feedforward network with area V1-like input-layer units and a Hebb rule can develop area MT-like second laye...
Martin I. Sereno, Margaret E. Sereno
ICPR
2010
IEEE
15 years 2 months ago
A Statistical Learning Approach to Spatial Context Exploitation for Semantic Image Analysis
In this paper, a statistical learning approach to spatial context exploitation for semantic image analysis is presented. The proposed method constitutes an extension of the key pa...
Georgios Th. Papadopoulos, Vasileios Mezaris, Ioan...