Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/74966
Title: Integrating eye-tracking technology with robust recurrent kernel online learning
Authors: Liu, Tiange
Keywords: DRNTU::Engineering
Issue Date: 2018
Abstract: 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.
URI: http://hdl.handle.net/10356/74966
Schools: School of Electrical and Electronic Engineering 
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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