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A variety of problems are related to real-world gesture recognition, such as continuous data streams, concept drift, novel and outlier samples, noise, scarcity of manually labeled data, on-line classification and the fact that the same gesture may implement in different way. Two important features should be included in the classifier to overcome these problems, which are the ability of detecting the...
The data in many real-world applications are streamed continuously which causes a variety of problems, e.g. Infinitely long data streams, concept drift, on-line or real-time classification and noise or outlier samples. To overcome these problems, the classifier should be updated continuously and it should have the ability to detect outliers. Since the size of the data set is growing with the duration...
The difficulties of data streams, i.e. Infinite length, the occurrence of concept-drift and the possible emergence of novel classes, are topics of high relevance in the field of recognition systems. To overcome all of these problems, the system should be updated continuously with new data while the amount of processing time should be kept small. We propose an incremental Parzen window kernel density...
The problems of data streaming, e.g. "infinite length" and "concept-drift", require incremental self-adapting classifiers. The performance of the classifier, however, is affected by false labels. Consequently, the classifier is required to detect outliers or samples belonging to unseen classes, i.e. novelties. We propose an incremental Mahalanobis distance based classifier using...
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