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Keywords: Sparse representation Implementations of L 0-norm Regularization term Support vector machine Kernel methods a b s t r a c t This paper provides a sparse learning algorithm for Support Vector Classification (SVC), called Sparse Support Vector Classification (SSVC), which leads to sparse solutions by automatically setting the irrelevant parameters(More)
– A Robocasting manufacturing process and robotic deposition machine are presented. The process requires that the machine be able to track 3-D trajectories with high precision. Iterative Learning Control (ILC) is presented as a viable strategy to meet these demands. Typically, practical implementation of ILC requires some type of Q-filtering that creates an(More)
Support vector machines (SVM) are state-of-the-art classifiers. Typically L2-norm or L1-norm is adopted as a regularization term in SVMs, while other norm-based SVMs, for example, the L0-norm SVM or even the L(infinity)-norm SVM, are rarely seen in the literature. The major reason is that L0-norm describes a discontinuous and nonconvex term, leading to a(More)
In order to achieve autonomous flight, micro helicopter should have the ability to estimate its state information. In this paper, a special target used as a landmark is designed and the corresponding recognition algorithm is developed, which mainly relies on the color feature. Besides, three algorithms of state estimation, linear and nonlinear optimization(More)
Color plane separation is very useful in processing color document images. Many reported methods take it as a multi-class classification problem and work not well in overlapped color regions. This paper proposed a simple but effective linear projection based method for separating overlapped color planes. The separation task is taken as a probability(More)