Add to ORKGIn this article, we propose a new supervised learning approach for pattern classification applications involving large or imbalanced data sets. In this approach, a clustering technique is employed to reduce the original training set into a smaller set of representative training exemplars, represented by weighted cluster centers and their target outputs. Based on the proposed learning approach, two training algorithms are derived for feed-forward neural networks. These algorithms are implemented and tested on two pattern classification applications- skin detection and image classification. Experimental results show that with the proposed learning approach, it is possible to design networks in a fraction of time taken by the standard learnin
In this paper we analyse the effect of introducing a structure in the input distribution on the gene...
In this paper, we discuss the role of clustering techniques in the design of neural networks. Specif...
This paper proposes a method for pre-training segmentation neural networks on small datasets using u...
In this article, we propose a new supervised learning approach for pattern classification applicatio...
This paper presents a new learning approach for pattern classification applications involving imbala...
In this paper a novel data mining algorithm, Clustering and Classification Algorithm-Supervised (CCA...
The paper categorizes and reviews the state-of-the-art approaches to the partially supervised learni...
Training data for supervised learning neural networks can be clustered such that the input/output pa...
Abstract. A method for training of an ML network for classification has been proposed by us in [3,4]...
2013-03-19Supervised learning is the machine learning task of inferring a function from labeled trai...
More and more datasets have increased their size with enough class annotations. Although the classif...
We present the clustering learning technique applied to multi-layer feedforward deep neural networks...
In Supervised training method, the training data is a pair consisting of an input object (typically ...
We propose a novel end-to-end neural network architecture that, once trained, directly outputs a pro...
Boosting is an iterative process that improves the predictive accuracy for supervised (machine) lear...
In this paper we analyse the effect of introducing a structure in the input distribution on the gene...
In this paper, we discuss the role of clustering techniques in the design of neural networks. Specif...
This paper proposes a method for pre-training segmentation neural networks on small datasets using u...
In this article, we propose a new supervised learning approach for pattern classification applicatio...
This paper presents a new learning approach for pattern classification applications involving imbala...
In this paper a novel data mining algorithm, Clustering and Classification Algorithm-Supervised (CCA...
The paper categorizes and reviews the state-of-the-art approaches to the partially supervised learni...
Training data for supervised learning neural networks can be clustered such that the input/output pa...
Abstract. A method for training of an ML network for classification has been proposed by us in [3,4]...
2013-03-19Supervised learning is the machine learning task of inferring a function from labeled trai...
More and more datasets have increased their size with enough class annotations. Although the classif...
We present the clustering learning technique applied to multi-layer feedforward deep neural networks...
In Supervised training method, the training data is a pair consisting of an input object (typically ...
We propose a novel end-to-end neural network architecture that, once trained, directly outputs a pro...
Boosting is an iterative process that improves the predictive accuracy for supervised (machine) lear...
In this paper we analyse the effect of introducing a structure in the input distribution on the gene...
In this paper, we discuss the role of clustering techniques in the design of neural networks. Specif...
This paper proposes a method for pre-training segmentation neural networks on small datasets using u...