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Difficulties of learning from nonstationary data stream are generally twofold. First, dynamically structured learning framework is required to catch up with the evolution of unstable class concepts, i.e., concept drifts. Second, imbalanced class distribution over data stream demands a mechanism to intensify the underrepresented class concepts for improved(More)
Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training(More)
In recent years, learning from imbalanced data has attracted growing attention from both academia and industry due to the explosive growth of applications that use and produce imbalanced data. However, because of the complex characteristics of imbalanced data, many real-world solutions struggle to provide robust efficiency in learning-based applications. In(More)
This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an image. Unlike the conventional multiple-object learning algorithms, the proposed method can automatically and adaptively learn from continuous video streams over the entire learning(More)
Ahstract-In this paper, an incremental self-organizing map integrated with hierarchical neural network (ISOM-HNN) is proposed as an efficient approach for signal classification in cognitive radio networks. This approach can effectively detect unknown radio signals in the uncertain communication environment. The adaptability of ISOM can improve the real­(More)
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