Graphs Regularization for Data Sets and Images: Filtering and Semi-Supervised Classification
author:
Vinh Thong Ta,
Université de Caen
Categories
Top: Computer Science: Machine Learning: Semi-supervised LearningTop: Computer Science: Image Analysis
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| Slides | |
| 0:00 | Graphs Regularization for Data Sets and Images: Filtering and Semi-Supervised Classification |
| 0:17 | Outline |
| 0:39 | What are the Main Ideas? |
| 1:01 | What are the Main Ideas? (2) |
| 1:19 | What are the Main Ideas? (3) |
| 1:27 | What are the Main Ideas? (4) |
| 1:37 | Graphs and Regularization Framework |
| 1:42 | What is a Weighted Graph? |
| 1:49 | What is a Weighted Graph? (2) |
| 1:52 | What is a Weighted Graph? (3) |
| 1:53 | What is a Weighted Graph? (4) |
| 2:12 | What is a Weighted Graph? (5) |
| 2:18 | Why Use Graph Representation? |
| 2:59 | Operators? |
| 3:09 | Operators? (2) |
| 3:18 | Operators? (3) |
| 3:29 | Operators? (4) |
| 3:33 | Weighted Graph Based Regularization? |
| 3:42 | Weighted Graph Based Regularization? (2) |
| 4:03 | Weighted Graph Based Regularization? (3) |
| 4:17 | Weighted Graph Based Regularization? (4) |
| 5:01 | Graph Based Regularization is Not New. . . |
| 5:34 | Applications |
| 5:40 | Filtering by Regularization |
| 6:18 | Filtering by Regularization (2) |
| 6:42 | Image Filtering: Classical Example |
| 6:53 | Image Filtering: Classical Example (2) |
| 7:14 | Data Set Filtering: A Toy Example |
| 7:39 | Data Set Filtering: A Toy Example (2) |
| 7:44 | Data Set Filtering: A Toy Example (3) |
| 8:02 | Data Set Filtering: UCI Data Bases |
| 8:20 | Data Set Filtering: UCI Data Bases (2) |
| 9:04 | Applications |
| 9:12 | Semi Supervised Classification by Regularization (1) |
| 9:35 | Semi Supervised Classification by Regularization (1) (2) |
| 9:59 | Semi Supervised Classification by Regularization (2) |
| 10:44 | The Two Moons Example |
| 10:56 | The Two Moons Example (2) |
| 11:12 | The Two Moons Example (3) |
| 11:29 | Image Semi Supervised Segmentation (1) |
| 11:55 | Image Semi Supervised Segmentation (1) (2) |
| 12:16 | Image Semi Supervised Segmentation (2) |
| 12:41 | Image Semi Supervised Segmentation (2) (2) |
| 13:10 | Conclusion |
| 13:28 | Conclusion (2) |
| 13:39 | Conclusion (3) |
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