Titlebook: Stream Data Mining: Algorithms and Their Probabilistic Properties; Leszek Rutkowski,Maciej Jaworski,Piotr Duda Book 2020 Springer Nature S

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The General Procedure of Ensembles Construction in Data Stream Scenariossulting with a few various persons. The vivid example is the diagnosis of an illness. When someone gets bad news, he often goes to other doctors for a second, third, fourth opinion and so on, until we are sure about the diagnosis.

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Basic Concepts of Probabilistic Neural Networks we should say that these methods have been almost completely replaced by the non-parametric approach (see e.g. [.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.]). In the non-parametric approach it is assumed that a functional form of probability densities is unknown.

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2197-6503 heuristics, it highlights methods and algorithms that are ma.This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes

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Misclassification Error Impurity Measureequality is the application of another statistical tool, e.g. the McDiarmid’s inequality. Another way is to find a split measure which can be expressed as an arithmetic average of some random variables since the Hoeffding’s inequality is applicable in this case. In the literature, many different imp