Hammer B, Micheli A, Sperduti A. Universal approximation capability of cascade correlation for structures. Neural Computation. 2005;17(5):1109-1159
We present the application of Cascade Correlation for structures to QSPR (quantitative structure-pro...
The Cascade-Correlation learning algorithm constructs a multi-layer artificial neural network as it ...
Determining network size used to require various ad hoc rules of thumb. In recent years, several res...
Cascade correlation (CC) constitutes a training method for neural networks which determines the weig...
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 ...
Hammer B, Gersmann K. A Note on the Universal Approximation Capability of Support Vector Machines. N...
This thesis is divided into two parts: the first examines various extensions to Cascade-Correlation,...
According to the characteristic that higher order derivatives of some base functions can be expresse...
Hammer B. On the approximation capability of recurrent neural networks. Neurocomputing. 2000;31(1-4)...
Hammer B. On the Approximation Capability of Recurrent Neural Networks. In: Heiss M, ed. Proceedings...
Hammer B, Sperschneider V. Neural networks can approximate mappings on structured objects. In: Wang ...
Neural network modeling typically ignores the role of knowledge in learning by starting from random ...
Abstract: In this paper we present a simple modification of some cascade-correlation type constructi...
Recurrent neural networks can simulate any finite state automata as well as any multi-stack Turing m...
We present the application of Cascade Correlation for structures to QSPR (quantitative structure-pro...
The Cascade-Correlation learning algorithm constructs a multi-layer artificial neural network as it ...
Determining network size used to require various ad hoc rules of thumb. In recent years, several res...
Cascade correlation (CC) constitutes a training method for neural networks which determines the weig...
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 ...
Hammer B, Gersmann K. A Note on the Universal Approximation Capability of Support Vector Machines. N...
This thesis is divided into two parts: the first examines various extensions to Cascade-Correlation,...
According to the characteristic that higher order derivatives of some base functions can be expresse...
Hammer B. On the approximation capability of recurrent neural networks. Neurocomputing. 2000;31(1-4)...
Hammer B. On the Approximation Capability of Recurrent Neural Networks. In: Heiss M, ed. Proceedings...
Hammer B, Sperschneider V. Neural networks can approximate mappings on structured objects. In: Wang ...
Neural network modeling typically ignores the role of knowledge in learning by starting from random ...
Abstract: In this paper we present a simple modification of some cascade-correlation type constructi...
Recurrent neural networks can simulate any finite state automata as well as any multi-stack Turing m...
We present the application of Cascade Correlation for structures to QSPR (quantitative structure-pro...
The Cascade-Correlation learning algorithm constructs a multi-layer artificial neural network as it ...
Determining network size used to require various ad hoc rules of thumb. In recent years, several res...
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