Two methods of obtaining structured information from a trained feed-forward neural network are discussed. The first of these methods extracts structured represen tations from the hidden layer and tests these against a set of hypotheses. The second method uses a rule-based system based on multiple-valued logic that can be trained using gradient descent. 1
對於神經網路系統將提出一個法則萃取的方式，並從神經網路中得到相關法則。在這裡我們所提到的方法是根據反函數的觀念而得到的。A rule-extraction method of the layered ...
We present an exact analysis of learning a rule by on-line gradient descent in a two-layered neural ...
Since the discovery of the back-propagation method, many modified and new algorithms have been propo...
The goal of data mining is to solve various problems dealing with knowledge extraction from huge amo...
The training of multilayer perceptron is generally a difficult task. Excessive training times and la...
This paper demonstrates how a multi-layer feed-forward network may be trained, using the method of g...
In this study, we focus on feed-forward neural networks with a single hidden layer. The research tou...
Title: Artificial Neural Networks and Their Usage For Knowledge Extraction Author: RNDr. Zuzana Petř...
In recent years, multi-layer feedforward neural networks have been popularly used for pattern classi...
Contrary to the common opinion, neural networks may be used for knowledge extraction. Recently, a ne...
Hybrid intelligent systems that combine knowledge based and artificial neural network systems typica...
This paper proposes an algorithm called Forward Composition Propagation (FCP) to explain the predict...
Abstract: This paper proposes a new method for explanation of tr ined neural networks feed forward t...
Before symbolic rules are extracted from a trained neural network, the network is usually pruned so ...
Despite their success-story, artificial neural networks have one major disadvantage compared to othe...
對於神經網路系統將提出一個法則萃取的方式，並從神經網路中得到相關法則。在這裡我們所提到的方法是根據反函數的觀念而得到的。A rule-extraction method of the layered ...
We present an exact analysis of learning a rule by on-line gradient descent in a two-layered neural ...
Since the discovery of the back-propagation method, many modified and new algorithms have been propo...
The goal of data mining is to solve various problems dealing with knowledge extraction from huge amo...
The training of multilayer perceptron is generally a difficult task. Excessive training times and la...
This paper demonstrates how a multi-layer feed-forward network may be trained, using the method of g...
In this study, we focus on feed-forward neural networks with a single hidden layer. The research tou...
Title: Artificial Neural Networks and Their Usage For Knowledge Extraction Author: RNDr. Zuzana Petř...
In recent years, multi-layer feedforward neural networks have been popularly used for pattern classi...
Contrary to the common opinion, neural networks may be used for knowledge extraction. Recently, a ne...
Hybrid intelligent systems that combine knowledge based and artificial neural network systems typica...
This paper proposes an algorithm called Forward Composition Propagation (FCP) to explain the predict...
Abstract: This paper proposes a new method for explanation of tr ined neural networks feed forward t...
Before symbolic rules are extracted from a trained neural network, the network is usually pruned so ...
Despite their success-story, artificial neural networks have one major disadvantage compared to othe...
對於神經網路系統將提出一個法則萃取的方式，並從神經網路中得到相關法則。在這裡我們所提到的方法是根據反函數的觀念而得到的。A rule-extraction method of the layered ...
We present an exact analysis of learning a rule by on-line gradient descent in a two-layered neural ...
Since the discovery of the back-propagation method, many modified and new algorithms have been propo...
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