Integrating eye-tracking technology with robust recurrent kernel online learning
This report is based on literature, Qing Song, et. Robust Recurrent Kernel Online Learning and Yanling Li, et. Integrating Eye-Tracking Technology with Robust Recurrent Kernel Online Learning. Robust recurrent kernel online learning (RRKOL) algorithm is used to investigate the integration of an eye...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/10356/74966 |
| _version_ | 1811682877888266240 |
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| author | Liu, Tiange |
| author2 | Song Qing |
| author_facet | Song Qing Liu, Tiange |
| author_sort | Liu, Tiange |
| collection | NTU |
| description | This report is based on literature, Qing Song, et. Robust Recurrent Kernel Online Learning and Yanling Li, et. Integrating Eye-Tracking Technology with Robust Recurrent Kernel Online Learning. Robust recurrent kernel online learning (RRKOL) algorithm is used to investigate the integration of an eye tracking technology. In Yanling’s article, there’re four versions of model.
1. F = 1 model, also known as 1 feedback, it performs classification with a 2-selection simulation of the eye-tracking system.
2. F = 0 model, also known as no feedback, it performs classification with a 2-selection simulation of the eye-tracking system.
3. F’= 1 model, it based on F= 1 model but takes in data from a 5-selection of simulation.
4. P = 1 model, it performs recurrent prediction with 1 feedback.
This project aims to repeat P = 1 model with 2-selection of simulation. |
| first_indexed | 2024-10-01T04:03:50Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/74966 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:03:50Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/749662023-07-07T17:39:02Z Integrating eye-tracking technology with robust recurrent kernel online learning Liu, Tiange Song Qing School of Electrical and Electronic Engineering DRNTU::Engineering This report is based on literature, Qing Song, et. Robust Recurrent Kernel Online Learning and Yanling Li, et. Integrating Eye-Tracking Technology with Robust Recurrent Kernel Online Learning. Robust recurrent kernel online learning (RRKOL) algorithm is used to investigate the integration of an eye tracking technology. In Yanling’s article, there’re four versions of model. 1. F = 1 model, also known as 1 feedback, it performs classification with a 2-selection simulation of the eye-tracking system. 2. F = 0 model, also known as no feedback, it performs classification with a 2-selection simulation of the eye-tracking system. 3. F’= 1 model, it based on F= 1 model but takes in data from a 5-selection of simulation. 4. P = 1 model, it performs recurrent prediction with 1 feedback. This project aims to repeat P = 1 model with 2-selection of simulation. Bachelor of Engineering 2018-05-25T06:06:28Z 2018-05-25T06:06:28Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/74966 en Nanyang Technological University 33 p. application/pdf |
| spellingShingle | DRNTU::Engineering Liu, Tiange Integrating eye-tracking technology with robust recurrent kernel online learning |
| title | Integrating eye-tracking technology with robust recurrent kernel online learning |
| title_full | Integrating eye-tracking technology with robust recurrent kernel online learning |
| title_fullStr | Integrating eye-tracking technology with robust recurrent kernel online learning |
| title_full_unstemmed | Integrating eye-tracking technology with robust recurrent kernel online learning |
| title_short | Integrating eye-tracking technology with robust recurrent kernel online learning |
| title_sort | integrating eye tracking technology with robust recurrent kernel online learning |
| topic | DRNTU::Engineering |
| url | http://hdl.handle.net/10356/74966 |
| work_keys_str_mv | AT liutiange integratingeyetrackingtechnologywithrobustrecurrentkernelonlinelearning |