The any-combiner is a classifier combination approach for target classification problems in which the target class can be naturally decomposed into multiple subclasses. This kind of classification problem can often occur in sensor-based system applications, such as biometric user verification, biosurveillance or underwater mine detection, in which the system goal is to identify a test exemplar as belonging to a category of objects of interest to the exclusion of all other exemplars (clutter). We propose an approach to the target classification problem in which an ensemble of classifier agents are trained to distinguish individual target subclasses from clutter. The any-combiner is then trained by optimizing the multi-agent ensemble for maximum recognition performance across all target subclasses over a range of acceptable operating points. Once deployed, the any-combiner classifies a test example as a target if any of the agents indicates a true positive classification for its target subclass. Experiments show that the any-combiner yields excellent performance on the tasks of biometric verification using face images and underwater object classification using acoustic features.<\/p>\n","protected":false},"excerpt":{"rendered":"
The any-combiner is a classifier combination approach for target classification problems in which the target class can be naturally decomposed into multiple subclasses. This kind of classification problem can often occur in sensor-based system applications, such as biometric user verification, biosurveillance or underwater mine detection, in which the system goal is to identify a test 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