Add to ORKGDasGupta B, Hammer B. Hardness of approximation of the loading problem for multi-layered feedforward neural networks. DIMACS Center, Rutgers University; 1999
International audienceWe algorithmically construct a two hidden layer feedforward neural network (TL...
This thesis is concerned with a numerical approximation technique for feedforward artificial neural ...
Neural Networks are widely noticed to provide a nonlinear function approximation method. In order to...
Hammer B. A NP-hardness result for a sigmoidal 3-node neural network. Osnabrücker Schriften zur Math...
AbstractWe deal with the problem of efficient learning of feedforward neural networks. First, we con...
We introduce the notion of suspect families of loading problems in the attempt of formalizing situat...
Multilayer feedforward neural nets with integer weights can be used to approximate the response of t...
AbstractWe formalize a notion of loading information into connectionist networks that characterizes ...
Hammer B. On the approximation capability of recurrent neural networks. Neurocomputing. 2000;31(1-4)...
We propose a new learning algorithm to enhance fault tolerance of multi-layer neural networks (MLN)....
We deal with computational issues of loading a fixed-architecture neural network with a set of posit...
Hammer B. On the Approximation Capability of Recurrent Neural Networks. In: Heiss M, ed. Proceedings...
In this article, we present a multiyariate two-layer feedforward neural networks that approximate co...
The training and synthesis of multilayer and multi-output feedforward artificial neural networks wit...
This paper deals with learnability of concept classes defined by neural networks, showing the hardne...
International audienceWe algorithmically construct a two hidden layer feedforward neural network (TL...
This thesis is concerned with a numerical approximation technique for feedforward artificial neural ...
Neural Networks are widely noticed to provide a nonlinear function approximation method. In order to...
Hammer B. A NP-hardness result for a sigmoidal 3-node neural network. Osnabrücker Schriften zur Math...
AbstractWe deal with the problem of efficient learning of feedforward neural networks. First, we con...
We introduce the notion of suspect families of loading problems in the attempt of formalizing situat...
Multilayer feedforward neural nets with integer weights can be used to approximate the response of t...
AbstractWe formalize a notion of loading information into connectionist networks that characterizes ...
Hammer B. On the approximation capability of recurrent neural networks. Neurocomputing. 2000;31(1-4)...
We propose a new learning algorithm to enhance fault tolerance of multi-layer neural networks (MLN)....
We deal with computational issues of loading a fixed-architecture neural network with a set of posit...
Hammer B. On the Approximation Capability of Recurrent Neural Networks. In: Heiss M, ed. Proceedings...
In this article, we present a multiyariate two-layer feedforward neural networks that approximate co...
The training and synthesis of multilayer and multi-output feedforward artificial neural networks wit...
This paper deals with learnability of concept classes defined by neural networks, showing the hardne...
International audienceWe algorithmically construct a two hidden layer feedforward neural network (TL...
This thesis is concerned with a numerical approximation technique for feedforward artificial neural ...
Neural Networks are widely noticed to provide a nonlinear function approximation method. In order to...