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» Improved Approximation of Linear Threshold Functions
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IJON
2007
184views more  IJON 2007»
13 years 9 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
STOC
1993
ACM
141views Algorithms» more  STOC 1993»
14 years 1 months ago
Bounds for the computational power and learning complexity of analog neural nets
Abstract. It is shown that high-order feedforward neural nets of constant depth with piecewisepolynomial activation functions and arbitrary real weights can be simulated for Boolea...
Wolfgang Maass
ECML
2005
Springer
14 years 3 months ago
Natural Actor-Critic
This paper investigates a novel model-free reinforcement learning architecture, the Natural Actor-Critic. The actor updates are based on stochastic policy gradients employing Amari...
Jan Peters, Sethu Vijayakumar, Stefan Schaal
WIOPT
2010
IEEE
13 years 7 months ago
Optimizing power allocation in interference channels using D.C. programming
Abstract--Power allocation is a promising approach for optimizing the performance of mobile radio systems in interference channels. In the present paper, the non-convex objective f...
Hussein Al-Shatri, Tobias Weber
PRL
2002
115views more  PRL 2002»
13 years 9 months ago
A generic fuzzy rule based image segmentation algorithm
Fuzzy rule based image segmentation techniques tend in general, to be application dependent with the structure of the membership functions being predefined and in certain cases, t...
Gour C. Karmakar, Laurence Dooley