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Most of data stream learning methods assume that a true class of an incoming instance is available right after it has been processed. However, assumption that we have an unlimited access to class labels is unrealistic and is directly connected with a very high labeling cost. This is a driving force behind growing development of methods that require reduced or no access to class labels. Among several...
Objects being recognized may arrive continuously to a classifier in the form of data stream, therefore contemporary classification systems have to make a decision not only on the basis of the static data, but on the data in motion as well. Additionally, we would like to start a classifier exploitation as soon as possible, then the models which can improve their models during exportation are very desirable...
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