The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
VLSI architectures for weighted order statistic (WOS) filters
Signal Processing
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Fuzzy stack filters-their definitions, fundamental properties, and application in image processing
IEEE Transactions on Image Processing
Lp norm design of stack filters
IEEE Transactions on Image Processing
Successive overrelaxation for support vector machines
IEEE Transactions on Neural Networks
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Weight assignment for adaptive image restoration by neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Extractive Support Vector Algorithm on Support Vector Machines for Image Restoration
Fundamenta Informaticae
Extractive Support Vector Algorithm on Support Vector Machines for Image Restoration
Fundamenta Informaticae
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Support vector machines (SVMs), a classification algorithm for the machine learning community, have been shown to provide higher performance than traditional learning machines. In this paper, the technique of SVMs is introduced into the design of weighted order statistics (WOS) filters. WOS filters are highly effective, in processing digital signals, because they have a simple window structure. However, due to threshold decomposition and stacking property, the development of WOS filters cannot significantly improve both the design complexity and estimation error. This paper proposes a new designing technique which can improve the learning speed and reduce the complexity of designing WOS filters. This technique uses a dichotomous approach to reduce the Boolean functions from 255 levels to two levels, which are separated by an optimal hyperplane, Furthermore, the optimal hyperplane is gotten by using the technique of SVMs. our proposed method approximates the optimal weighted order statistics filters more rapidly than the adaptive neural filters.