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

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Hybrid Splitting Criteria measure (or, more precisely, the corresponding split measure function). Therefore we will refer to such criteria as ‘single’ splitting criteria. The experiments conducted in Chap. . demonstrate that various single splitting criteria have their own advantages and drawbacks. Based on this observation

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Basic Concepts of Probabilistic Neural Networksifties and sixties, problems of statistical pattern classification in the stationary case were accomplished by means of parametric methods, using the available apparatus of statistical mathematics (e.g. [.,.,.,.,.]). The knowledge of the probability density to an accuracy of unknown parameters was a

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General Non-parametric Learning Procedure for Tracking Concept Drift learning in non-stationary environments where occasionally published in the sixties and seventies. The proper tool for solving such a type of problems seemed to be the dynamic stochastic approximation technique [., .] as an extension of the Robbins-Monro [.] procedure for the non-stationary case. T

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Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networksthem deal with a non-stationary regression. Most of them rely on the Gaussian or Markov models, extend Support Vector Machine or Extreme Learning Machine to regression problems, implement regression trees or polynomial regression for working in a non-stationary environment. We will briefly describe

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Probabilistic Neural Networks for the Streaming Data Classificationhough there exist a lot of methods for classification of static datasets, they can hardly be adapted to deal with data streams. This is due to the features of the data stream such as potentially infinite volume, fast rate of data arrival and the occurrence of concept drift.

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The General Procedure of Ensembles Construction in Data Stream Scenarios. However, in many cases, the fastest algorithms are less accurate than methods requiring high computational power and more time for data analysis. Therefore, to enhance the performance of the algorithms, which in data stream scenario must be characterized by low memory requirement and short time of

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Regression is a lack of new approaches to creating ensembles of regression estimators [., .]. Most of the latest developments focus on the application of the regression estimators to solve very important real-world problems. In [.] the authors propose to create an ensemble composed of decision trees, gradient

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Leszek Rutkowski,Maciej Jaworski,Piotr Dudar clinical trials. This chapter offers a unified basis for the analysis of marker and response data, emphasizing the central importance of the correlation, or linkage disequilibrium, between SNP markers and the genes that affect response. It is convenient to phrase the development of association map