25 search results - page 1 / 5 » Learning Deep Neural Networks for High Dimensional Output Pr... |
ICMLA
2009
13 years 4 months ago
2009
State-of-the-art pattern recognition methods have difficulty dealing with problems where the dimension of the output space is large. In this article, we propose a new framework ba...
IJCNN
2006
IEEE
14 years 25 days ago
2006
IEEE
— This paper shows the advantage of using neural network modularity over conventional learning schemes to approximate complex functions. Indeed, it is difficult for artificial ...
SBRN
2008
IEEE
14 years 1 months ago
2008
IEEE
The design of an autonomous navigation system for mobile robots can be a tough task. Noisy sensors, unstructured environments and unpredictability are among the problems which mus...
NPL
2002
13 years 6 months ago
2002
A novel multistage feedforward network is proposed for efficient solving of difficult classification tasks. The standard Radial Basis Functions (RBF) architecture is modified in or...
JMLR
2012
11 years 9 months ago
2012
In this paper, we propose a second order optimization method to learn models where both the dimensionality of the parameter space and the number of training samples is high. In ou...