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TNN
2008
138views more  TNN 2008»
15 years 4 months ago
A Fast and Scalable Recurrent Neural Network Based on Stochastic Meta Descent
This brief presents an efficient and scalable online learning algorithm for recurrent neural networks (RNNs). The approach is based on the real-time recurrent learning (RTRL) algor...
Zhenzhen Liu, Itamar Elhanany
EAAI
2006
157views more  EAAI 2006»
15 years 5 months ago
Color reduction and estimation of the number of dominant colors by using a self-growing and self-organized neural gas
A new method for color reduction in a digital image is proposed, which is based on the development of a new neural network classifier and on a new method for Estimation of the Mos...
Antonios Atsalakis, Nikos Papamarkos
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
NIPS
2008
15 years 6 months ago
Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning
Randomized neural networks are immortalized in this well-known AI Koan: In the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6. "What a...
Ali Rahimi, Benjamin Recht
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ICTAI
2003
IEEE
15 years 10 months ago
Q-Concept-Learning: Generalization with Concept Lattice Representation in Reinforcement Learning
One of the very interesting properties of Reinforcement Learning algorithms is that they allow learning without prior knowledge of the environment. However, when the agents use al...
Marc Ricordeau