Add to ORKGCascade correlation (CC) constitutes a training method for neural networks which determines the weights as well as the neural architec-ture during training. Various extensions of CC to structured data have been proposed: recurrent cascade correlation (RCC) for sequences, re-cursive cascade correlation (RecCC) for tree structures with limited ¤ This work has been partially supported by MIUR grant 2002093941 004. We would like to thank two anonymous referees for profound and valuable comments on an earlier version of the manuscript
Recurrent neural networks can simulate any finite state automata as well as any multi-stack Turing m...
Constructive algorithms have proved to be powerful methods for training feedforward neural networks....
Abstract: In this paper we present a simple modification of some cascade-correlation type constructi...
Cascade correlation (CC) constitutes a training method for neural networks that determines the weigh...
Hammer B, Micheli A, Sperduti A. Universal approximation capability of cascade correlation for struc...
Abstract: "Cascade-Correlation is a new architecture and supervised learning algorithm for artificia...
This paper is an overview of cascade-correlation neural networks which form a specific class inside ...
Recurrent Cascade-Correlation (RCC) is a recurrent version of the Cascade-Correlation learning archi...
This thesis is divided into two parts: the first examines various extensions to Cascade-Correlation,...
Neural network modeling typically ignores the role of knowledge in learning by starting from random ...
It is often difficult to predict the optimal neural network size for a particular application. Const...
It is often difficult to predict the optimal neural network size for a particular application. Const...
It is often difficult to predict the optimal neural network size for a particular application, Const...
An experimental investigation of the cascade-correlation network (CC) is carried out in different be...
We discuss the weight update rule in the Cascade Correlation neural net learning algorithm. The weig...
Recurrent neural networks can simulate any finite state automata as well as any multi-stack Turing m...
Constructive algorithms have proved to be powerful methods for training feedforward neural networks....
Abstract: In this paper we present a simple modification of some cascade-correlation type constructi...
Cascade correlation (CC) constitutes a training method for neural networks that determines the weigh...
Hammer B, Micheli A, Sperduti A. Universal approximation capability of cascade correlation for struc...
Abstract: "Cascade-Correlation is a new architecture and supervised learning algorithm for artificia...
This paper is an overview of cascade-correlation neural networks which form a specific class inside ...
Recurrent Cascade-Correlation (RCC) is a recurrent version of the Cascade-Correlation learning archi...
This thesis is divided into two parts: the first examines various extensions to Cascade-Correlation,...
Neural network modeling typically ignores the role of knowledge in learning by starting from random ...
It is often difficult to predict the optimal neural network size for a particular application. Const...
It is often difficult to predict the optimal neural network size for a particular application. Const...
It is often difficult to predict the optimal neural network size for a particular application, Const...
An experimental investigation of the cascade-correlation network (CC) is carried out in different be...
We discuss the weight update rule in the Cascade Correlation neural net learning algorithm. The weig...
Recurrent neural networks can simulate any finite state automata as well as any multi-stack Turing m...
Constructive algorithms have proved to be powerful methods for training feedforward neural networks....
Abstract: In this paper we present a simple modification of some cascade-correlation type constructi...