The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Statistical Modeling Approach to Location Estimation
IEEE Transactions on Mobile Computing
Kernel Matrix Completion by Semidefinite Programming
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
AINA '03 Proceedings of the 17th International Conference on Advanced Information Networking and Applications
Robotics-based location sensing using wireless Ethernet
Wireless Networks
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Wireless Geolocation Systems and Services
IEEE Communications Magazine
Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks
IEEE Journal on Selected Areas in Communications
Engineering Applications of Artificial Intelligence
Online modeling based on support vector machine
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
A dynamic system approach for radio location fingerprinting in wireless local area networks
IEEE Transactions on Communications
A grid-based algorithm for on-device GSM positioning
Proceedings of the 12th ACM international conference on Ubiquitous computing
Landmark-assisted location and tracking in outdoor mobile network
Multimedia Tools and Applications
MANET location prediction using machine learning algorithms
WWIC'12 Proceedings of the 10th international conference on Wired/Wireless Internet Communication
Kernel-based particle filtering for indoor tracking in WLANs
Journal of Network and Computer Applications
Uncaught signal imputation for accuracy enhancement of WLAN-based positioning systems
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
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Location estimation using the Global System for Mobile communication (GSM) is an emerging application that infers the location of the mobile receiver from multiple signals measurements. While geometrical and signal propagation models have been deployed to tackle this estimation problem, the terrain factors and power fluctuations have confined the accuracy of such estimation. Using support vector regression, we investigate the missing value location estimation problem by providing theoretical and empirical analysis on existing and novel kernels. A novel synthetic experiment is designed to compare the performances of different location estimation approaches. The proposed support vector regression approach shows promising performances, especially in terrains with local variations in environmental factors.