… We know that a complex network classification problem often includes many samples and
… specific network structure, so we cannot directly use GCN to classify networks. 2.4 Network …

… classification. Our experiments are based on benchmarking datasets minist [1] and cifar-10.
On the basis of the Convolutional neural network, … of the influence on image classification. …

… Abstract—Image patch classification is an important task in many different medical imaging
… customized Convolutional Neural Networks (CNN) with shallow convolution layer to classify …

… beats, and their correct classification into different diagnoses, … We developed a 9-layer deep
convolutional neural network (… % in the diagnostic classification of heartbeats in original and …

… classification and retrieval method, based on the PANORAMA [PPTP10] 3D shape descriptor
and a convolutional neural network … fed to a convolutional neural network (CNN) [LBBH98…

… Convolutional Neural Network in the classification of Batik. After discussing, this analysis ends
with conclusions about the Convolutional Neural Network … the convolution neural network …

… convolutional neural network (CNN) is a neural network variant that consists of a number
of convolutional … , reduce data dimensionality, and classify in one network structure. Such a …

… -improving convolution neural network for the classification of … Yu, “Exploring convolutional
neural network structures and … Ramakrishnan, SB Williams, and CT Jin, “Convolutional neural …

… a novel approach that coupled a deep convolutional neural network (CNN) with a seemingly
… CNN modelling for a powder XRD pattern classification enabled us to predict the crystal …

… on convolutional neural network (CNN), have gained widespread application in plant disease
classification… They have solved or partially solved the problems of traditional classification …