Name
Abstract | ||
|---|---|---|
In the big data era, image processing for large dataset becomes an issue that requires immediate solution. We proposed an effective solution for training a deep convolutional neural network on Apache Spark, successfully reduced the processing time by nearly a half also retained a high recognition accuracy at the meantime. This network model could also prevent overfitting by applying dropout algorithm. Experiments are performed on MNIST dataset to make comparisons with different Convolutional Neural Networks (CNN) architectures in multiple dimensions thoroughly. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICMLA.2018.00092 | 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) |
Keywords | Field | DocType |
Convolutional Neural Network,Deep Learning,Dropout,Parallel Training | Spark (mathematics),MNIST database,Pattern recognition,Computer science,Convolutional neural network,Image processing,Artificial intelligence,Deep learning,Overfitting,Big data,Network model,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-6806-1 | 0 | 0.34 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingyi Shen | 1 | 1 | 1.02 |
| M. Omair Shafiq | 2 | 139 | 18.59 |