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IJON
2007
184views more  IJON 2007»
13 years 7 months ago
Convex incremental extreme learning machine
Unlike the conventional neural network theories and implementations, Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden n...
Guang-Bin Huang, Lei Chen
COLT
2001
Springer
14 years 9 hour ago
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
We investigate the use of certain data-dependent estimates of the complexity of a function class, called Rademacher and Gaussian complexities. In a decision theoretic setting, we ...
Peter L. Bartlett, Shahar Mendelson
NEUROSCIENCE
2001
Springer
13 years 12 months ago
Role of the Cerebellum in Time-Critical Goal-Oriented Behaviour: Anatomical Basis and Control Principle
The Brain is a slow computer yet humans can skillfully play games such as tennis where very fast reactions are required. Of particular interest is the evidence for strategic thinki...
Guido Bugmann
ICASSP
2011
IEEE
12 years 11 months ago
Adaptive modelling with tunable RBF network using multi-innovation RLS algorithm assisted by swarm intelligence
— In this paper, we propose a new on-line learning algorithm for the non-linear system identification: the swarm intelligence aided multi-innovation recursive least squares (SIM...
Hao Chen, Yu Gong, Xia Hong
ISCAS
1999
IEEE
114views Hardware» more  ISCAS 1999»
13 years 11 months ago
Channel equalization by feedforward neural networks
A signal su ers from nonlinear, linear, and additive distortion when transmitted through a channel. Linear equalizers are commonly used in receivers to compensate for linear chann...
Biao Lu, Brian L. Evans