Machine-Learning Applications of Algorithmic Randomness
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Transduction with Confidence and Credibility
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A Probabilistic Active Support Vector Learning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Support vector machine techniques for nonlinear equalization
IEEE Transactions on Signal Processing
Support vector machine multiuser receiver for DS-CDMA signals in multipath channels
IEEE Transactions on Neural Networks
Incremental training of support vector machines
IEEE Transactions on Neural Networks
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Recently, the feasibility of using support vector machines (SVMs) for multiuser detection in code division multiple access (CDMA) systems has been investigated. Previous results show that SVMs perform well with short training sequences but suffer from two drawbacks that are highly undesirable in real-time applications: the run-time complexity and the block-based learning. To deal with these problems, here we propose a sample-by-sample adaptive algorithm for CDMA systems based on incremental SVMs, incorporating an active learning strategy aimed to reduce the complexity of both the training phase and the final classifier.