Sensing-Aware Kernel SVM

2 Dec 2013Weicong DingPrakash IshwarVenkatesh SaligramaW. Clem Karl

We propose a novel approach for designing kernels for support vector machines (SVMs) when the class label is linked to the observation through a latent state and the likelihood function of the observation given the state (the sensing model) is available. We show that the Bayes-optimum decision boundary is a hyperplane under a mapping defined by the likelihood function... (read more)

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