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ACM Transactions on Information Systems (TOIS)
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The nature of statistical learning theory
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Polynomial bounds for VC dimension of sigmoidal neural networks
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Data Mining and Knowledge Discovery
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IEEE Transactions on Mobile Computing
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ECML '98 Proceedings of the 10th European Conference on Machine Learning
Support Vector Machines: Training and Applications
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EURASIP Journal on Applied Signal Processing
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EURASIP Journal on Advances in Signal Processing
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Pervasive and Mobile Computing
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IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
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In this paper, techniques and algorithms developed in the framework of Statistical Learning Theory are applied to the problem of determining the location of a wireless device by measuring the signal strength values from a set of access points (location fingerprinting). Statistical Learning Theory provides a rich theoretical basis for the development of models starting from a set of examples. Signal strength measurement is part of the normal operating mode of wireless equipment, in particular Wi-Fi, so that no special-purpose hardware is required.The proposed techniques, based on the Support Vector Machine paradigm, have been implemented and compared, on the same data set, with other approaches considered in scientific literature. Tests performed in a real-world environment show that results are comparable, with the advantage of a low algorithmic complexity in the normal operating phase. Moreover, the algorithm is particularly suitable for classification, where it outperforms the other techniques.