@inproceedings{942e6a3352074ba38305516d251399e2,
title = "Robust extreme learning machine for regression problems with its application to wifi based indoor positioning system",
abstract = "We propose two kinds of robust extreme learning machines (RELMs) based on the close-to-mean constraint and the small-residual constraint respectively to solve the problem of noisy measurements in indoor positioning systems (IPSs). We formulate both RELMs as second order cone programming problems. The fact that feature mapping in ELM is known to users is exploited to give the needed information for robust constraints. Real-world indoor localization experimental results show that, the proposed algorithms can not only improve the accuracy and repeatability, but also reduce the deviations and worst case errors of IPSs compared with basic ELM and OPT-ELM based IPSs.",
keywords = "Indoor positioning system, Robust extreme learning machine, Second order cone programming",
author = "Xiaoxuan Lu and Yushen Long and Han Zou and Chengpu Yu and Lihua Xie",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 24th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2014 ; Conference date: 21-09-2014 Through 24-09-2014",
year = "2014",
month = nov,
day = "14",
doi = "10.1109/MLSP.2014.6958903",
language = "English",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
editor = "Mamadou Mboup and Tulay Adali and Eric Moreau and Jan Larsen",
booktitle = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
address = "United States",
}