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» Learning Approximate Consistencies
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158
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IJON
2007
184views more  IJON 2007»
15 years 4 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
148
Voted
ECML
2006
Springer
15 years 8 months ago
Transductive Gaussian Process Regression with Automatic Model Selection
Abstract. In contrast to the standard inductive inference setting of predictive machine learning, in real world learning problems often the test instances are already available at ...
Quoc V. Le, Alexander J. Smola, Thomas Gärtne...
119
Voted
ICML
2010
IEEE
15 years 5 months ago
Clustering processes
The problem of clustering is considered, for the case when each data point is a sample generated by a stationary ergodic process. We propose a very natural asymptotic notion of co...
Daniil Ryabko
ADCM
2006
74views more  ADCM 2006»
15 years 4 months ago
Linearly constrained reconstruction of functions by kernels with applications to machine learning
This paper investigates the approximation of multivariate functions from data via linear combinations of translates of a positive definite kernel from a reproducing kernel Hilbert...
Robert Schaback, J. Werner
IJON
2008
118views more  IJON 2008»
15 years 4 months ago
Incremental extreme learning machine with fully complex hidden nodes
Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks 17(4) (2006) 879
Guang-Bin Huang, Ming-Bin Li, Lei Chen, Chee Kheon...