Titlebook: Innovations in Classification, Data Science, and Information Systems; Proceedings of the 2 Daniel Baier (Chair of Marketing and Innovation

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書目名稱Innovations in Classification, Data Science, and Information Systems影響因子(影響力)




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書目名稱Innovations in Classification, Data Science, and Information Systems被引頻次




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書目名稱Innovations in Classification, Data Science, and Information Systems讀者反饋




書目名稱Innovations in Classification, Data Science, and Information Systems讀者反饋學(xué)科排名

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Innovations in Classification, Data Science, and Information Systems978-3-540-26981-6Series ISSN 1431-8814 Series E-ISSN 2198-3321

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Studies in Classification, Data Analysis, and Knowledge Organizationhttp://image.papertrans.cn/i/image/467053.jpg

潛伏期

https://doi.org/10.1007/b138406Analysis; Time series; clustering; controlling; data analysis; information system; learning; production; sta

FIN

Regression Clustering with Redescending M-Estimatorsusters. We work out the method not only for classical regression but also for orthogonal regression and multivariate locations and give consistency results for all three cases. The approach of orthogonal regression is applied to the identification of edges in noisy images.

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Symbolic Classifier with Convex Hull Based Dissimilarity Function (or a set of regions) in ?. defined by the convex hull of the objects belonging to this class. In the allocation step, the assignment of a new object to a class is based on a dissimilarity matching function that compares the class description (a region or a set of regions) with a point in ?.. This

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