Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/74966
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dc.contributor.authorLiu, Tiange-
dc.date.accessioned2018-05-25T06:06:28Z-
dc.date.available2018-05-25T06:06:28Z-
dc.date.issued2018-
dc.identifier.urihttp://hdl.handle.net/10356/74966-
dc.description.abstractThis 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.en_US
dc.format.extent33 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University-
dc.subjectDRNTU::Engineeringen_US
dc.titleIntegrating eye-tracking technology with robust recurrent kernel online learningen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorSong Qingen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
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item.grantfulltextrestricted-
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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