A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Detection and identification of odorants using an electronic nose
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Reducing the effect of out-voting problem in ensemble based incremental support vector machines
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Support Vector Machines (SVMs) have been applied to solve the classification of volatile organic compounds (VOC) data in some recent studies. SVMs provide good generalization performance in detection and classification of VOC data. However, in many applications involving VOC data, it is not unusual for additional data, which may include new classes, to become available over time, which then requires an SVM classifier that is capable of incremental learning that does not suffer from loss of previously acquired knowledge. In our previous work, we have proposed the incremental SVM approach based on Learn++.MT. In this contribution, the ability of SVMLearn++.MT to incrementally classify VOC data is evaluated and compared against a similarly constructed Learn++.MT algorithm that uses radial basis function neural network as base classifiers.