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Dynamic environments require adaptive applications. One particular machinelearning problem in dynamic environments is open world recognition. Itcharacterizes a continuously changing domain where only some classes are seenin one batch of the training data and such batches can only be learnedincrementally. Open world recognition is a demanding task that is, to the bestof our knowledge, addressed by only a few methods. This work introduces amodification of the widely known Extreme Value Machine (EVM) to enable openworld recognition. Our proposed method extends the EVM with a partial modelfitting function by neglecting unaffected space during an update. This reducesthe training time by a factor of 28. In addition, we provide a modified modelreduction using weighted maximum K-set cover to strictly bound the modelcomplexity and reduce the computational effort by a factor of 3.5 from 2.1 s to0.6 s. In our experiments, we rigorously evaluate openness with two novelevaluation protocols. The proposed method achieves superior accuracy of about12 % and computational efficiency in the tasks of image classification and facerecognition.

Exploring the Open World Using Incremental Extreme Value Machines