[PDF] Neural Networks and Statistical Models | Semantic Scholar

The characteristics and organization of neural networks are presented, and the article shows why they are an attractive alternative to regression.

The characteristics and organization of neural networks are presented, and the article shows why they are an attractive alternative to regression.

This talk shows how to fit neural networks using SAS/OR R, SAS/ETS R, and SAS/STAT R software.

This talk shows how to fit neural networks using SAS/OR R, SAS/ETS R, and SAS/STAT R software.

This chapter provides an overview of neural network models and their applications to data mining tasks and presents three important classes of neural models including feedforward multilayer networks, Hopfield networks, and Kohonen’s self-organizing maps.

This chapter provides an overview of neural network models and their applications to data mining tasks and presents three important classes of neural models including feedforward multilayer networks, Hopfield networks, and Kohonen’s self-organizing maps.

This paper proposed multi-backpropagation network to reduce the size of a large backpropagations network, which is split into several smaller networks, which act as a specialized network.

This paper proposed multi-backpropagation network to reduce the size of a large backpropagations network, which is split into several smaller networks, which act as a specialized network.

The experiments presented in this paper illustrate the application of discrimination techniques using MLP discriminants to neural network trained to solve supervised learning task such as the Launch Interceptor Condition 1 problem and indicate that directed splitting or usingMLP discriminant is an important strategy in improving generalization of the networks.

The experiments presented in this paper illustrate the application of discrimination techniques using MLP discriminants to neural network trained to solve supervised learning task such as the Launch Interceptor Condition 1 problem and indicate that directed splitting or usingMLP discriminant is an important strategy in improving generalization of the networks.

Several examples are presented showing how an ANN can be used to represent an ARMA scheme and the forecasting abilities of feedforward and recurrent neu-ral networks with traditional methods are compared.

Several examples are presented showing how an ANN can be used to represent an ARMA scheme and the forecasting abilities of feedforward and recurrent neu-ral networks with traditional methods are compared.

It was found out that the mean square error attached to ANNmodel was smaller than regression model which made ANN a better model in prediction.

It was found out that the mean square error attached to ANNmodel was smaller than regression model which made ANN a better model in prediction.

Applying Artificial Neural Networks to Business, Economics and Finance

• Yochanan Shachmurove

• Economics

This paper surveys the significance of recent work on emulative neural networks (ENNs) by researchers across many disciplines in the light of issues of indeterminacy. Financial and economic

…

• 44

• PDF