Probabilistic Latent Document Network Embedding
Publisher:
Year
: 2014DOI: 10.1109/ICCE-TW.2014.6904057
Keyword(s): computational complexity,n image classification,n image sampling,n learning (artificial intelligence),n object detection,n pattern clustering,n pedestrians,n support vector machines,n HOG features,n SURF points,n SVM-classifier,n cascade-Adaboost structure,n deformable part models,n detection failure,n k-means clustering scheme,n nonrobust features,n object detection,n pedestrian detection,n shift with importance sampling technique,n time
Collections
:
-
Statistics
Probabilistic Latent Document Network Embedding
Show full item record
| contributor author | Le, Tuan M.V. | |
| contributor author | Lauw, Hady W. | |
| date accessioned | 2020-03-12T22:46:38Z | |
| date available | 2020-03-12T22:46:38Z | |
| date issued | 2014 | |
| identifier other | 7023344.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1094201?locale-attribute=en | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | Probabilistic Latent Document Network Embedding | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8233896 | |
| subject keywords | computational complexity | |
| subject keywords | n image classification | |
| subject keywords | n image sampling | |
| subject keywords | n learning (artificial intelligence) | |
| subject keywords | n object detection | |
| subject keywords | n pattern clustering | |
| subject keywords | n pedestrians | |
| subject keywords | n support vector machines | |
| subject keywords | n HOG features | |
| subject keywords | n SURF points | |
| subject keywords | n SVM-classifier | |
| subject keywords | n cascade-Adaboost structure | |
| subject keywords | n deformable part models | |
| subject keywords | n detection failure | |
| subject keywords | n k-means clustering scheme | |
| subject keywords | n nonrobust features | |
| subject keywords | n object detection | |
| subject keywords | n pedestrian detection | |
| subject keywords | n shift with importance sampling technique | |
| subject keywords | n time | |
| identifier doi | 10.1109/ICCE-TW.2014.6904057 | |
| journal title | ata Mining (ICDM), 2014 IEEE International Conference on | |
| filesize | 1684229 | |
| citations | 0 |