<![CDATA[Incremental learning aims to learn new classes if they emerge while maintaining the performance for previously known classes. It acquires useful information from incoming data to update the existing models. Open-set recognition, however, requires the ability to recognize examples from known classes and reject examples from new/unknown classes. In this work, we propose a combination of multiclass open-set recognition and an incremental learning scheme in the audio recognition domain. We introduce incremental open-set multiclass support vector machine algorithms that can classify examples from seen/unseen classes, using incremental learning to increase the current model with new classes without entirely retraining the system. Comprehensive evaluations carried out on problems of multi-class open-set recognition showed promising performance for the proposed methods, compared with some representative previous methods]]>
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