In this article we are going to discuss the improvement of the multi classes- classification problem using multi layer Perceptron. The considered approach consists in breaking down the n-class problem into two-classes- subproblems. The training of each two-class subproblem is made independently; as for the phase of test, we are going to confront a vector that we want to classify to all two classes- models, the elected class will be the strongest one that won-t lose any competition with the other classes. Rates of recognition gotten with the multi class-s approach by two-class-s decomposition are clearly better that those gotten by the simple multi class-s approach
Traditional artificial neural architectures possess limited ability to address the scale problem exh...
A training data selection method for multi-class data is proposed. This method can be used for multi...
One connectionist approach to the classification problem, which has gained popularity in recent year...
International audienceA decomposition approach to multiclass classification problems consists in dec...
The Letter reports the benefits of decomposing the multilayer perceptron (MLP) for pattern recogniti...
Multi-class classification is the classification task where separates samples into more than 2 class...
Abstract—We present a new method of multiclass classification based on the combination of one-vs-all...
Multiclass classification is a fundamental and challenging task in machine learning. The existing te...
feed-forward multilayer neural network is a generalisation of the single layer perceptron topology. ...
Abstract The new C-Mantec algorithm constructs com-pact neural network architectures for classsifica...
Several researchers have proposed effective approaches for binary classification in the last years. ...
The paper describes what to consider when constructing multi-classifier systems (MCS), what is perce...
This paper gives a general insight into how the neuron structure in a multilayer perceptron (MLP) ca...
Neural networks, particularly Multilayer Pereceptrons (MLPs) have been found to be successful for va...
Abst rac t. In this paper, we propose a new methodology for decompos-ing pattern classification prob...
Traditional artificial neural architectures possess limited ability to address the scale problem exh...
A training data selection method for multi-class data is proposed. This method can be used for multi...
One connectionist approach to the classification problem, which has gained popularity in recent year...
International audienceA decomposition approach to multiclass classification problems consists in dec...
The Letter reports the benefits of decomposing the multilayer perceptron (MLP) for pattern recogniti...
Multi-class classification is the classification task where separates samples into more than 2 class...
Abstract—We present a new method of multiclass classification based on the combination of one-vs-all...
Multiclass classification is a fundamental and challenging task in machine learning. The existing te...
feed-forward multilayer neural network is a generalisation of the single layer perceptron topology. ...
Abstract The new C-Mantec algorithm constructs com-pact neural network architectures for classsifica...
Several researchers have proposed effective approaches for binary classification in the last years. ...
The paper describes what to consider when constructing multi-classifier systems (MCS), what is perce...
This paper gives a general insight into how the neuron structure in a multilayer perceptron (MLP) ca...
Neural networks, particularly Multilayer Pereceptrons (MLPs) have been found to be successful for va...
Abst rac t. In this paper, we propose a new methodology for decompos-ing pattern classification prob...
Traditional artificial neural architectures possess limited ability to address the scale problem exh...
A training data selection method for multi-class data is proposed. This method can be used for multi...
One connectionist approach to the classification problem, which has gained popularity in recent year...