… , a hyperspectral image classification method with convolutional neural network and random
… , and each individual classifier tries to classify the data properly by using the CNNs. Some …

… under the constraints imposed by commodity hardware and size of the image collection. We
show that the deep convolutional neural network approach to domain specific classification …

… This paper combines an integrated learning algorithm with a deep convolutional neural
network. The model uses the Baging integrated classifier to maintain an independent, parallel …

… a new method using genetic algorithms for evolving the architectures and connection weight
initialization values of a deep convolutional neural network to address image classification …

… visual attention to finegrained classification task using deep neural network. Our pipeline …
deep nets, then use it to improve both the what and where aspects. Importantly, we avoid using …

… networks of millions of parameters, which has in turn led to significant improvements in image
classification[… house number digit classification [19]. Additionally, features learned by large …

… convolution neural network, it is used in image classification. There are some problems such
as noise, lack of labeled samples, the tendency to overfitting, a lack of extraction of spectral …

… is to perform image classification using CNNs on … image classification accuracy is obtained
by our framework while requiring 2GB memory only, making our framework ideal to be used …

… In this paper, the performance of the proposed Convolutional Neural Network (CNN) with
three well-known image recognition methods such as Principal Component Analysis (PCA), …

… In this paper, we propose a surrogate-assisted classification method to classify retinal OCT
images automatically based on convolutional neural networks (CNNs). Image denoising is …