Add to ORKGAbstract — In this paper, we propose a new method for de-composing pattern classification problems based on the class relations among training data. By using this method, we can divide a K-class classification problem into a series of K 2 two-class problems. These two-class problems are to discriminate clas
This article describes an approach to designing a distributed and modular neural classifier. This ap...
Abstract — Task decomposition is a widely used method to solve complex and large problems. In this p...
Abstract. The min-max modular network has been shown to be an efficient classifier, especially in so...
Abst rac t. In this paper, we propose a new methodology for decompos-ing pattern classification prob...
An N-class problem can be fully decomposed into N independent small neural networks called modules (...
In pattern recognition, the approach where Supervised Learning is reduced to the construction of dec...
International audienceA decomposition approach to multiclass classification problems consists in dec...
In this paper, we propose a new task decomposition method for multilayered feedforward neural networ...
Decomposing a complex computational problem into sub-problems, which are computationally simpler to ...
A technique has been devised and tested which allows separate training of neural network (NN) module...
Task Decomposition with Pattern Distributor (PD) is a new task decomposition method for multilayered...
In this article we are going to discuss the improvement of the multi classes- classification problem...
Abstract. In applications such as character recognition, some classes are heavily overlapped but are...
Modular neural networks have the possibility of overcoming common scalability and interference probl...
Abstract—Task decomposition with pattern distributor (PD) is a new task decomposition method for mul...
This article describes an approach to designing a distributed and modular neural classifier. This ap...
Abstract — Task decomposition is a widely used method to solve complex and large problems. In this p...
Abstract. The min-max modular network has been shown to be an efficient classifier, especially in so...
Abst rac t. In this paper, we propose a new methodology for decompos-ing pattern classification prob...
An N-class problem can be fully decomposed into N independent small neural networks called modules (...
In pattern recognition, the approach where Supervised Learning is reduced to the construction of dec...
International audienceA decomposition approach to multiclass classification problems consists in dec...
In this paper, we propose a new task decomposition method for multilayered feedforward neural networ...
Decomposing a complex computational problem into sub-problems, which are computationally simpler to ...
A technique has been devised and tested which allows separate training of neural network (NN) module...
Task Decomposition with Pattern Distributor (PD) is a new task decomposition method for multilayered...
In this article we are going to discuss the improvement of the multi classes- classification problem...
Abstract. In applications such as character recognition, some classes are heavily overlapped but are...
Modular neural networks have the possibility of overcoming common scalability and interference probl...
Abstract—Task decomposition with pattern distributor (PD) is a new task decomposition method for mul...
This article describes an approach to designing a distributed and modular neural classifier. This ap...
Abstract — Task decomposition is a widely used method to solve complex and large problems. In this p...
Abstract. The min-max modular network has been shown to be an efficient classifier, especially in so...