The active badge location system
ACM Transactions on Information Systems (TOIS)
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise classification and support vector machines
Advances in kernel methods
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
LANDMARC: indoor location sensing using active RFID
Wireless Networks - Special issue: Pervasive computing and communications
RFID: A Technical Overview and Its Application to the Enterprise
IT Professional
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Application areas of AIS: The past, the present and the future
Applied Soft Computing
Expert Systems with Applications: An International Journal
Advances in artificial immune systems
IEEE Computational Intelligence Magazine
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
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This study intends to propose a hybrid of artificial immune system (AIS) and particle swarm optimization (PSO)-based support vector machine (SVM) (HIP-SVM) for optimizing SVM parameters, and applied it to radio frequency identification (RFID)-based positioning system. In order to evaluate HIP-SVM's capability, six benchmark data sets, Australian, Heart disease, Iris, Ionosphere, Sonar and Vowel, were employed. The computational results showed that HIP-SVM has better performance than AIS-based SVM and PSO-based SVM. HIP-SVM was also applied to classify RSSI for indoor positioning. The experiment results indicated that HIP-SVM can achieve highest accuracy compared to those of AIS-SVM and PSO-SVM. It demonstrated that RFID can be used for storing information and in indoor positioning without additional cost.