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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)
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)
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)