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• W3144489360 abstract "Wi-Fi-based indoor localization with high capability and feasibility needs to implement lifelong online learning mechanism. However, the characteristic of Wi-Fi is wide variability, which lies in not only the fluc- tuation of signal strength value, but also the increase or decrease in the number of access points (APs). The tradi- tional algorithms are effective for signal fluctuation, but cannot handle the dimension-changing problem of features caused by increase and decrease in APs' number. To solve this problem, we propose a Feature Adaptive Online Sequential Extreme Learning Machine (FA-OSELM) algorithm. It can transfer the original model to a new one with a small number of data with new features, so as to make the new model suitable for the new feature dimen- sion. The experiments show that the FA-OSELM can get higher accuracy with a small amount of new data, and it is an effective method to make lifelong indoor localization practical." @default.
• W3144489360 created "2021-04-13" @default.
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• W3144489360 date "2016-01-01" @default.
• W3144489360 modified "2023-09-27" @default.
• W3144489360 title "Feature Adaptive Online Sequential Extreme Learning Machine for lifelong indoor localization" @default.
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• W3144489360 hasPublicationYear "2016" @default.
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