Add to ORKGIn this paper a fast method of selecting a neural network architecture for pattern recognition tasks is presented. We demonstrate that our proposed method of selecting both input features and hidden neurons avoids the pitfalls exhibited by other methods reported in the literature. It is also shown that the resulting network architecture is extremely lean while at the same time signif-icantly improving the network performance. The resulting solution provides a very useful tool which is now being incorporated in the operations system used for large image database surveys.
Deep learning (DL) is a class of machine learning algorithms that relies on deep neural networks (DN...
A unified methodology for categorizing various complex objects is presented in this book. Through pr...
Designing neural networks for object recognition requires considerable architecture engineering. As ...
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Th...
Forecasting, classification, and data analysis may all gain from improved pattern recognition result...
In the deep-learning community, new algorithms are published at a very fast pace. Therefore, solving...
We describe a self-organizing framework for the generation of a network useful in content-based retr...
Deep Neural Networks have received considerable attention in recent years. As the complexity of netw...
Neural architecture search (NAS) can have a significant impact in computer vision by automatically d...
BigEarthNet is one of the standard large remote sensing datasets. It has been shown previously that ...
Deep learning has become in recent years a cornerstone tool fueling key innovations in the industry,...
To achieve excellent performance with modern neural networks, having the right network architecture ...
In recent years, deep learning (DL) has been widely studied using various methods across the globe, ...
Neural Architecture Search (NAS), i.e., the automation of neural network design, has gained much pop...
Neural Architecture Search (NAS), i.e., the automation of neural network design, has gained much pop...
Deep learning (DL) is a class of machine learning algorithms that relies on deep neural networks (DN...
A unified methodology for categorizing various complex objects is presented in this book. Through pr...
Designing neural networks for object recognition requires considerable architecture engineering. As ...
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Th...
Forecasting, classification, and data analysis may all gain from improved pattern recognition result...
In the deep-learning community, new algorithms are published at a very fast pace. Therefore, solving...
We describe a self-organizing framework for the generation of a network useful in content-based retr...
Deep Neural Networks have received considerable attention in recent years. As the complexity of netw...
Neural architecture search (NAS) can have a significant impact in computer vision by automatically d...
BigEarthNet is one of the standard large remote sensing datasets. It has been shown previously that ...
Deep learning has become in recent years a cornerstone tool fueling key innovations in the industry,...
To achieve excellent performance with modern neural networks, having the right network architecture ...
In recent years, deep learning (DL) has been widely studied using various methods across the globe, ...
Neural Architecture Search (NAS), i.e., the automation of neural network design, has gained much pop...
Neural Architecture Search (NAS), i.e., the automation of neural network design, has gained much pop...
Deep learning (DL) is a class of machine learning algorithms that relies on deep neural networks (DN...
A unified methodology for categorizing various complex objects is presented in this book. Through pr...
Designing neural networks for object recognition requires considerable architecture engineering. As ...