This paper presents a new neuro-fuzzy classifier, inspired by the Simpson's (1992, 1993) min-max model. By relying on a constructive approach, it overcomes some undesired properties of the original min-max algorithm. In particular, training result does not depend on pattern presentation order and hyperbox expansion is not limited by a fixed maximum size, so that it is possible to have different covering resolutions. Consequently, the new algorithm yields less complex networks, thus increasing the generalization capability in accordance with learning theory paradigms. Several tests are presented for illustration
In the present paper, a new algorithm to train Min-Max neural models is proposed. It is based on the...
At present, pattern classification is one of the most important aspects of establishing machine inte...
We propose a constructive method, inspired by Simpson's min-max technique (1992), for obtaining fuzz...
A new neuro-fuzzy classifier, inspired by the min-max neural model, is presented. The classification...
A high automation degree is one of the most important features of data driven modeling tools and it ...
Classification can be considered as a basic data driven modeling problem, which allows us to define ...
Although the most important feature of a classifier is its generalization capability, the effectiven...
In this thesis we studied two of the most promising neural network classifiers called as fuzzy min-m...
The fuzzy min-max (FMM) neural network is one of the most powerful neural networks that combines neu...
An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed ...
An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed ...
© 2020 IEEE. This paper proposes an improved version of the current online learning algorithm for a ...
This paper proposes a method to accelerate the training process of general fuzzy min-max neural netw...
In this paper, we propose two new neuro--fuzzy schemes, one for classification and one for clusterin...
In this paper, a boosted Fuzzy Min-Max Neural Network (FMM) is proposed. While FMM is a learning alg...
In the present paper, a new algorithm to train Min-Max neural models is proposed. It is based on the...
At present, pattern classification is one of the most important aspects of establishing machine inte...
We propose a constructive method, inspired by Simpson's min-max technique (1992), for obtaining fuzz...
A new neuro-fuzzy classifier, inspired by the min-max neural model, is presented. The classification...
A high automation degree is one of the most important features of data driven modeling tools and it ...
Classification can be considered as a basic data driven modeling problem, which allows us to define ...
Although the most important feature of a classifier is its generalization capability, the effectiven...
In this thesis we studied two of the most promising neural network classifiers called as fuzzy min-m...
The fuzzy min-max (FMM) neural network is one of the most powerful neural networks that combines neu...
An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed ...
An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed ...
© 2020 IEEE. This paper proposes an improved version of the current online learning algorithm for a ...
This paper proposes a method to accelerate the training process of general fuzzy min-max neural netw...
In this paper, we propose two new neuro--fuzzy schemes, one for classification and one for clusterin...
In this paper, a boosted Fuzzy Min-Max Neural Network (FMM) is proposed. While FMM is a learning alg...
In the present paper, a new algorithm to train Min-Max neural models is proposed. It is based on the...
At present, pattern classification is one of the most important aspects of establishing machine inte...
We propose a constructive method, inspired by Simpson's min-max technique (1992), for obtaining fuzz...