WALD-Kernel: A method for learning sequential detectors | IEEE Conference Publication | IEEE Xplore

WALD-Kernel: A method for learning sequential detectors


Abstract:

We consider the problem of training a binary sequential classifier under an error rate constraint. It is well known that for known densities, accumulating the likelihood ...Show More

Abstract:

We consider the problem of training a binary sequential classifier under an error rate constraint. It is well known that for known densities, accumulating the likelihood ratio statistics is time optimal under a fixed error rate constraint. For the case of unknown densities, we formulate the learning for sequential detection problem as a constrained density ratio estimation problem. Specifically, we show that the problem can be posed as a convex optimization problem using a Reproducing Kernel Hilbert Space (RKHS) representation for the log-density ratio function. The proposed binary sequential classifier is tested on a synthetic data set and four real world data sets, together with previous approaches for density ratio estimation. Our empirical results show that the classifier trained through the proposed technique achieves smaller average sampling cost than previous classifiers proposed in the literature for the same error rate.
Date of Conference: 26-29 June 2016
Date Added to IEEE Xplore: 25 August 2016
ISBN Information:
Conference Location: Palma de Mallorca, Spain

References

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