Name
Title | ||
|---|---|---|
A Comparison between Artificial Neural Network and Cascade-Correlation Neural Network in Concept Classification |
Abstract | ||
|---|---|---|
Deep learning has achieved significant attention recently due to promising results in representing and classifying concepts most prominently in the form of convolutional neural networks CNN. While CNN has been widely studied and evaluated in computer vision, there are other forms of deep learning algorithms which may be promising. One interesting deep learning approach which has received relatively little attention in visual concept classification is Cascade-Correlation Neural Networks CCNN. In this paper, we create a visual concept retrieval system which is based on CCNN. Experimental results on the CalTech101 dataset indicate that CCNN outperforms ANN. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-13168-9_26 | PCM |
Field | DocType | Citations |
Pattern recognition,Physical neural network,Computer science,Convolutional neural network,Deep belief network,Time delay neural network,Artificial intelligence,Cascade correlation,Deep learning,Artificial neural network,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanming Guo | 1 | 128 | 13.06 |
| Liang Bai | 2 | 379 | 36.34 |
| Song-Yang Lao | 3 | 206 | 21.58 |
| Song Wu | 4 | 90 | 5.58 |
| Michael S. Lew | 5 | 2742 | 166.02 |