Incremental kernel learning algorithms and applications.

Since the Support Vector Machines (SVMs) were introduced in 1995, SVMs have been recognized as essential tools for pattern classification and function approximation. Numerous publications show that SVMs outperform other learning methods in various areas. However, SVMs have a weak performance with large-scale data sets because of high computational complexity. One approach to overcome this limitation is the incremental learning approach where a large-scale data set is divided into several subsets and trained on those subsets updating the core information extracted from the previous subset. This approach also has a drawback that the core information is accumulated during the incremental procedure. When the large-scale data set has a special structure (e.g., in the case of unbalanced data set), the standard SVM might not perform properly. In this study, a novel approach based on the reduced convex hull concept is developed and applied in various applications. In addition, the developed concept is applied to the Support Vector Regression (SVR) to produce better performance. From the performed experiments, the incremental revised SVM significantly reduces the number of support vectors and requires less computing time. In addition the incremental revised SVR produces similar results with the standard SVR by reducing computing time significantly. Furthermore, the filter concept developed in this study may be utilized to reduce the computing time in other learning approach

Abnormal Gait Behavior Detection for Elderly Based on Enhanced Wigner-Ville Analysis and Cloud Incremental SVM Learning

A cloud based health care system is proposed in this paper for the elderly by providing abnormal gait behavior detection, classification, online diagnosis, and remote aid service. Intelligent mobile terminals with triaxial acceleration sensor embedded are used to capture the movement and ambulation information of elderly. The collected signals are first enhanced by a Kalman filter. And the magnitude of signal vector features is then extracted and decomposed into a linear combination of enhanced Gabor atoms. The Wigner-Ville analysis method is introduced and the problem is studied by joint time-frequency analysis. In order to solve the large-scale abnormal behavior data lacking problem in training process, a cloud based incremental SVM (CI-SVM) learning method is proposed. The original abnormal behavior data are first used to get the initial SVM classifier. And the larger abnormal behavior data of elderly collected by mobile devices are then gathered in cloud platform to conduct incremental training and get the new SVM classifier. By the CI-SVM learning method, the knowledge of SVM classifier could be accumulated due to the dynamic incremental learning. Experimental results demonstrate that the proposed method is feasible and can be applied to aged care, emergency aid, and related fields