Titlebook: Discovery Science; 23rd International C Annalisa Appice,Grigorios Tsoumakas,Stan Matwin Conference proceedings 2020 Springer Nature Switzer

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Unsupervised Concept Drift Detection Using a Student–Teacher Approachm monitor the loss of predictive models. Accordingly, an alarm is launched when the loss increases significantly, which triggers some adaptation mechanism (e.g. retrain the model). However, this . falls short in many real-world scenarios, where the true labels are not readily available to compute th

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Conference proceedings 2020tions named: classification; clustering; data and knowledge representation; data streams; distributed processing; ensembles; explainable and interpretable machine learning; graph and network mining; multi-target models; neural networks and deep learning; and spatial, temporal and spatiotemporal data..

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Bernd-Christian Funk,Bernd Schilcherualitative preliminary study on a collection of artworks made by Pablo Picasso shows the effectiveness of the model. The proposed method may assist in art-related tasks, in particular visual link retrieval and historical knowledge discovery in painting datasets.

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Iterative Multi-mode Discretization: Applications to Co-clusteringnd more accurate than state-of-the-art methods. We demonstrate the performance of IMMD-CC in comparison to several state-of-the-art methods on 100 data sets from a benchmark cohort, as well as 35 real-world datasets. The results show the promising potential of the proposed method.

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