Danian Zheng

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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 exactly to zero. SSVC adopts the L0-norm regularization term and is trained by an iteratively reweighted learning algorithm. We show(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)
NNSRM is an implementation of the structural risk minimization (SRM) principle using the nearest neighbor (NN) rule, and linear discriminant analysis (LDA) is a dimension-reducing method, which is usually used in classifications. This paper combines the two methods for face recognition. We first project the face images into a PCA subspace, then project the(More)
Highly accurate optical character recognition (OCR) of handwritten characters is still a challenging task, especially for languages like Chinese and Japanese. To improve the accuracy, we developed four techniques for enhanced recognition: character recognition based on modified linear discriminant analysis (MLDA), subspace-based similar-character(More)