The nature of statistical learning theory
The nature of statistical learning theory
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Study on Least Squares Support Vector Machines Algorithm and Its Application
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Support vector machine techniques for nonlinear equalization
IEEE Transactions on Signal Processing
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
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We apply a support vector machine (SVM) classifier to the design of analog to digital converters. Each output bit of the converter is the output of a binary classifier, which is a nonlinear SVM. The classifier effectively learns a folding characteristic for each bit, which is realized as the weighted sum of a set of kernel functions. In our proposal, the kernel does not need to be symmetric or positive definite, unlike in the case of a conventional SVM. This makes the approach more amenable to VLSI design, where such constraints are hard to satisfy. The SVM uses a set of binary weights, which allows the folding characteristics to be digitally corrected after fabrication. This facility is of considerable value in analog design in a deep sub micron era, where simulation models do not adequately capture the behavior of devices, or their variations. The proposed methodology reduces design time, can be automated and calibrated for process variations and ageing, by changing a set of digital scaling coefficients.