A Classification Framework for Anomaly Detection
The Journal of Machine Learning Research
QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines
The Journal of Machine Learning Research
Support Vector Machines
The Journal of Machine Learning Research
Learning from only positive and unlabeled data to detect lesions in vascular CT images
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
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We consider the query by multiple example problem where the goal is to identify database samples whose content is similar to a collection of query samples. To assess the similarity we use a relative content density which quantifies the relative concentration of the query distribution to the database distribution. If the database distribution is a mixture of the query distribution and a background distribution then it can be shown that database samples whose relative content density is greater than a particular threshold ρ are more likely to have been generated by the query distribution than the background distribution. We describe an algorithm for predicting samples with relative content density greater than ρ that is computationally efficient and possesses strong performance guarantees. We also show empirical results for applications in computer network monitoring and image segmentation.