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rutkowski l data mining algorithms 2020

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Textbook in PDF format 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 how to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Table of contents Introduction and Overview of the Main Results of the Book Basic Concepts of Data Stream Mining Decision Trees in Data Stream Mining Splitting Criteria Based on the McDiarmid’s Theorem Misclassification Error Impurity Measure Splitting Criteria with the Bias Term Hybrid Splitting Criteria Basic Concepts of Probabilistic Neural Networks General Non-parametric Learning Procedure for Tracking Concept Drift Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks Probabilistic Neural Networks for the Streaming Data Classification The General Procedure of Ensembles Construction in Data Stream Scenarios Classification Regression Final Remarks and Challenging Problems

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Rutkowski L. Stream Data Mining. Algorithms and Their Probabilistic Properties 2020.pdf 10.7 MB

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rutkowski l data mining algorithms 2020