An online algorithm for training LS-SVM (Least Square Support Vector Machines) was proposed for the application of function estimation and classification. Online LS-SVM means that LS-SVM can be trained in an incremental way, and can be pruned to get sparse approximation in a decremental way. When a SV (Support Vector) is added or removed, the online algorithm avoids computing large-scale matrix inverse. Thus the computation cost is reduced. Online algorithm is especially useful to realistic function estimation problem such as system identification. The experiments with benchmark function estimation problem and classification problem show the validity of this online algorithm.
A face recognition system based on Support Vector Machine (SVM) and Hidden Markov Model (HMM) has been proposed. The powerful discriminative ability of SVM is combined with the temporal modeling ability of HMM. The output of SVM is moderated to be probability output, which replaces the Mixture of Gauss (MOG) in HMM. Wavelet transformation is used to extract observation vector, which reduces the data dimension and improves the robustness.The hybrid system is compared with pure HMM face recognition method based on ORL face database and Yale face database. Experiments results show that the hybrid method has better performance.
