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https://hdl.handle.net/10216/88482| Author(s): | John Michael Salgado Cebola |
| Title: | Pre-trained Convolutional Networks and generative statiscial models: a study in semi-supervised learning |
| Issue Date: | 2016-07-19 |
| Description: | Comparative study between the performance of Convolutional Networks using pretrained models and statistical generative models on tasks of image classification in semi-supervised enviroments. Study of multiple ensembles using these techniques and generated data from estimated pdfs. Pretrained Convents, LDA, pLSA, Fisher Vectors, Sparse-coded SPMs, TSVMs being the key models worked upon. |
| Subject: | Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
| Scientific areas: | Ciências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática Engineering and technology::Electrical engineering, Electronic engineering, Information engineering |
| DOI: | 10.34626/ars2-pp29 |
| TID identifier: | 201309084 |
| URI: | https://hdl.handle.net/10216/88482 |
| Document Type: | Dissertação |
| Rights: | openAccess |
| Appears in Collections: | FEUP - Dissertação |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 170137.pdf | Pre-trained Convolutional Networks and generative statiscial models: a study in semi-supervised learning | 6.22 MB | Adobe PDF |  View/Open |
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