Add to ORKGA general method for building and training multilayer perceptrons composed of linear threshold units is proposed. A simple recursive rule is used to build the structure of the network by adding units as they are needed, while a modified perceptron algorithm is used to learn the connection strengths. Convergence to zero errors is guaranteed for any boolean classification on patterns of binary variables. Simulations suggest that this method is efficient in terms of the numbers of units constructed, and the networks it builds can generalize over patterns not in the training set.
A fast iterative algorithm is proposed for the construction and the learning of a neural net achievi...
Abstract—A new efficient computational technique for training of multilayer feedforward neural netwo...
A critical question in the neural network research today concerns how many hidden neurons to use. Th...
A general method for building and training multilayer perceptrons composed of linear threshold units...
The training of multilayer perceptron is generally a difficult task. Excessive training times and la...
In this paper, we propose a genetic algorithm for the training and construction of a multilayer perc...
Abstract—We develop, in this brief, a new constructive learning algorithm for feedforward neural net...
The perceptron is essentially an adaptive linear combiner with the output quantized to ...
This study highlights on the subject of weight initialization in multi-layer feed-forward networks....
Abstract- We propose a novel learning algorithm to train networks with multi-layer linear-threshold ...
The use of multilayer perceptrons (MLP) with threshold functions (binary step function activations) ...
The back propagation algorithm caused a tremendous breakthrough in the application of multilayer per...
In recent years, multi-layer feedforward neural networks have been popularly used for pattern classi...
Artificial neural networks have, in recent years, been very successfully applied in a wide range of ...
In this paper, we review neural networks, models of neural networks, methods for selecting neural ne...
A fast iterative algorithm is proposed for the construction and the learning of a neural net achievi...
Abstract—A new efficient computational technique for training of multilayer feedforward neural netwo...
A critical question in the neural network research today concerns how many hidden neurons to use. Th...
A general method for building and training multilayer perceptrons composed of linear threshold units...
The training of multilayer perceptron is generally a difficult task. Excessive training times and la...
In this paper, we propose a genetic algorithm for the training and construction of a multilayer perc...
Abstract—We develop, in this brief, a new constructive learning algorithm for feedforward neural net...
The perceptron is essentially an adaptive linear combiner with the output quantized to ...
This study highlights on the subject of weight initialization in multi-layer feed-forward networks....
Abstract- We propose a novel learning algorithm to train networks with multi-layer linear-threshold ...
The use of multilayer perceptrons (MLP) with threshold functions (binary step function activations) ...
The back propagation algorithm caused a tremendous breakthrough in the application of multilayer per...
In recent years, multi-layer feedforward neural networks have been popularly used for pattern classi...
Artificial neural networks have, in recent years, been very successfully applied in a wide range of ...
In this paper, we review neural networks, models of neural networks, methods for selecting neural ne...
A fast iterative algorithm is proposed for the construction and the learning of a neural net achievi...
Abstract—A new efficient computational technique for training of multilayer feedforward neural netwo...
A critical question in the neural network research today concerns how many hidden neurons to use. Th...