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» A Quantitative Theory of Neural Computation
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ICCV
2009
IEEE
14 years 9 months ago
Kernel map compression using generalized radial basis functions
The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machin...
Omar Arif, Patricio A. Vela
AR
2007
138views more  AR 2007»
14 years 12 months ago
Integrating robotics and neuroscience: brains for robots, bodies for brains
—Researchers in robotics and artificial intelligence have often looked at biology as a source of inspiration for solving their problems. From the opposite perspective, neuroscie...
Michele Rucci, Daniel Bullock, Fabrizio Santini
ISCI
2000
92views more  ISCI 2000»
14 years 11 months ago
Quantum associative memory
This paper combines quantum computation with classical neural network theory to produce a quantum computational learning algorithm. Quantum computation uses microscopic quantum lev...
Dan Ventura, Tony R. Martinez
CHI
2008
ACM
16 years 7 days ago
Peephole pointing: modeling acquisition of dynamically revealed targets
Peephole interaction occurs when a spatially aware display is moved and acts as a viewport to reveal different parts of the virtual space that cannot all fit within the display at...
Xiang Cao, Jacky Jie Li, Ravin Balakrishnan
PPSN
1994
Springer
15 years 3 months ago
Convergence Models of Genetic Algorithm Selection Schemes
We discuss the use of normal distribution theory as a tool to model the convergence characteristics of di erent GA selection schemes. The models predict the proportion of optimal a...
Dirk Thierens, David E. Goldberg