Add to ORKG© Published under licence by IOP Publishing Ltd. Deep neural networks with a large number of parameters are a powerful tool for solving problems of pattern recognition, prediction and classification. Nevertheless, overfitting remains a serious problem in the use of such networks. A method of solving the problem of overfitting is proposed in this article. This method is based on reducing the number of independent parameters of a neural network model using the principal component analysis, and can be implemented using existing libraries of neural computing. The algorithm was tested on the problem of recognition of handwritten symbols from the MNIST database, as well as on the task of predicting time series (rows of the average monthly number ...
Overfitting is one issue that deep learning faces in particular. It leads to highly accurate classif...
Developments in deep learning with ANNs (Artificial Neural Networks) are paving the way for revoluti...
In this paper we apply a Neural Network (NN) to reduce image dataset,distilling the massive datasets...
© Published under licence by IOP Publishing Ltd. Deep neural networks with a large number of paramet...
Forecasting, classification, and data analysis may all gain from improved pattern recognition result...
Machine learning consists in the creation and development of algorithms that allow a machine to lear...
Unsupervised neural networks, such as restricted Boltzmann machines (RBMs) and deep belief networks ...
Artificial Neural Networks (ANN) consists of some components, such as architecture and learning alg...
The recent success of large and deep neural network models has motivated the training of even larger...
Artificial neural networks (ANN) are typically composed of a large number of nonlinear functions (ne...
An important problem in a classification system is how to get good accuracy results. A way to increa...
The analysis of a time series is a problem well known to statisticians. Neural networks form the bas...
Overfitting is a common problem in neural networks. This report uses a simple neural network to do s...
Nonlinear principal component analysis (NLPCA) can be performed by a neural network model which nonl...
Complex data analysis is becoming more easily accessible to analytical chemists, including natural c...
Overfitting is one issue that deep learning faces in particular. It leads to highly accurate classif...
Developments in deep learning with ANNs (Artificial Neural Networks) are paving the way for revoluti...
In this paper we apply a Neural Network (NN) to reduce image dataset,distilling the massive datasets...
© Published under licence by IOP Publishing Ltd. Deep neural networks with a large number of paramet...
Forecasting, classification, and data analysis may all gain from improved pattern recognition result...
Machine learning consists in the creation and development of algorithms that allow a machine to lear...
Unsupervised neural networks, such as restricted Boltzmann machines (RBMs) and deep belief networks ...
Artificial Neural Networks (ANN) consists of some components, such as architecture and learning alg...
The recent success of large and deep neural network models has motivated the training of even larger...
Artificial neural networks (ANN) are typically composed of a large number of nonlinear functions (ne...
An important problem in a classification system is how to get good accuracy results. A way to increa...
The analysis of a time series is a problem well known to statisticians. Neural networks form the bas...
Overfitting is a common problem in neural networks. This report uses a simple neural network to do s...
Nonlinear principal component analysis (NLPCA) can be performed by a neural network model which nonl...
Complex data analysis is becoming more easily accessible to analytical chemists, including natural c...
Overfitting is one issue that deep learning faces in particular. It leads to highly accurate classif...
Developments in deep learning with ANNs (Artificial Neural Networks) are paving the way for revoluti...
In this paper we apply a Neural Network (NN) to reduce image dataset,distilling the massive datasets...