Rutkowski L Data Mining Algorithms 2020 torrent

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