In a previous simulation study, the complexity of neural networks for limited cases of binary and normally-distributed variables based the null distribution of the likelihood ratio statistic and the corresponding chi-square distribution was characterized. This study expands on those results and presents a more general formulation for calculating degrees of freedom
In this paper, we present the feed-forward neural network (FFNN) and recurrent neural network (RNN) ...
We survey and summarize the existing literature on the computational aspects of neural network mode...
This dissertation is designed to answer the following questions: (1) Which measurement model is bett...
Despite recent publications exploring model complexity with modern regression methods, their dimensi...
The notion of equivalent number of degrees of freedom (e.d.f.) has been recently proposed in the con...
The focus of this paper is on the neural network modelling approach that has gained increasing recog...
The notion of equivalent number of degrees of freedom (e.d.f.) has been recently proposed in the con...
The focus of this paper is on the neural network modelling approach that has gained increasing recog...
Neural networks provide a more flexible approximation of functions than traditional linear regressio...
that has attracted a number of researchers is the mathematical evaluation of neural networks as info...
Using richly parameterised models for small datasetscan be justified from a theoretical point of vie...
Summary. In Ingrassia and Morlini (2005) we have suggested the notion of equivalent num-ber of degre...
Abstract In this paper, we explore degrees of freedom in deep sigmoidal neural networks. We show tha...
Capabilities of linear and neural-network models are compared from the point of view of requirements...
Learning curves show how a neural network is improved as the number of training examples increases a...
In this paper, we present the feed-forward neural network (FFNN) and recurrent neural network (RNN) ...
We survey and summarize the existing literature on the computational aspects of neural network mode...
This dissertation is designed to answer the following questions: (1) Which measurement model is bett...
Despite recent publications exploring model complexity with modern regression methods, their dimensi...
The notion of equivalent number of degrees of freedom (e.d.f.) has been recently proposed in the con...
The focus of this paper is on the neural network modelling approach that has gained increasing recog...
The notion of equivalent number of degrees of freedom (e.d.f.) has been recently proposed in the con...
The focus of this paper is on the neural network modelling approach that has gained increasing recog...
Neural networks provide a more flexible approximation of functions than traditional linear regressio...
that has attracted a number of researchers is the mathematical evaluation of neural networks as info...
Using richly parameterised models for small datasetscan be justified from a theoretical point of vie...
Summary. In Ingrassia and Morlini (2005) we have suggested the notion of equivalent num-ber of degre...
Abstract In this paper, we explore degrees of freedom in deep sigmoidal neural networks. We show tha...
Capabilities of linear and neural-network models are compared from the point of view of requirements...
Learning curves show how a neural network is improved as the number of training examples increases a...
In this paper, we present the feed-forward neural network (FFNN) and recurrent neural network (RNN) ...
We survey and summarize the existing literature on the computational aspects of neural network mode...
This dissertation is designed to answer the following questions: (1) Which measurement model is bett...
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