Journal ArticleDOI
Adaptive least squares support vector machines filter for hand tremor canceling in microsurgery
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TLDR
With the LS-SVMAF, the least squares support vector machines adaptive filter, this paper can model and predict the hand tremor more effectively and improve the precision and reliability in the master–slave robotic system for microsurgery.Abstract:
One of the main problems for effective control of a minimally invasive surgery (MIS) is the imprecision that caused by hand tremor. In this paper, a novel adaptive filter, the least squares support vector machines adaptive filter (LS-SVMAF), is proposed to overcome this problem. Compared with traditional methods like multi layer perceptron (MLP), LS-SVM shows a superior performance of nonlinear modeling with small scale of data set or high dimensional input space. With the LS-SVMAF, we can model and predict the hand tremor more effectively and improve the precision and reliability in the master–slave robotic system for microsurgery. Simulation results demonstrate the effectiveness of the proposed filter and its superior performance over its competing rivals.read more
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References
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Book
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
Proceedings ArticleDOI
Blind Image Restoration Using a Block-Stationary Signal Model
Tom E. Bishop,James R. Hopgood +1 more
TL;DR: A novel method for blind image restoration which is a multidimensional extension of an approach used successfully for audio restoration, and a maximum marginalised a posteriori (MMAP) blur estimate is obtained by optimising the resulting probability density function.
Journal ArticleDOI
Medical robotics in computer-integrated surgery
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Proceedings ArticleDOI
Proximal support vector machine classifiers
Glenn Fung,Olvi L. Mangasarian +1 more
TL;DR: Computational results on publicly available datasets indicate that the proposed proximal SVM classifier has comparable test set correctness to that of standard S VM classifiers, but with considerably faster computational time that can be an order of magnitude faster.