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Learning from Data Streams in Evolving Environments

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Cover of 'Learning from Data Streams in Evolving Environments'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Transfer Learning in Non-stationary Environments
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    Chapter 3 A New Combination of Diversity Techniques in Ensemble Classifiers for Handling Complex Concept Drift
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    Chapter 4 Analyzing and Clustering Pareto-Optimal Objects in Data Streams
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    Chapter 5 Error-Bounded Approximation of Data Stream: Methods and Theories
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    Chapter 6 Ensemble Dynamics in Non-stationary Data Stream Classification
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    Chapter 7 Processing Evolving Social Networks for Change Detection Based on Centrality Measures
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    Chapter 8 Large-Scale Learning from Data Streams with Apache SAMOA
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    Chapter 9 Process Mining for Analyzing Customer Relationship Management Systems: A Case Study
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    Chapter 10 Detecting Smooth Cluster Changes in Evolving Graph Structures
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    Chapter 11 Efficient Estimation of Dynamic Density Functions with Applications in Data Streams
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    Chapter 12 Incremental SVM Learning: Review
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    Chapter 13 On Social Network-Based Algorithms for Data Stream Clustering
Attention for Chapter 4: Analyzing and Clustering Pareto-Optimal Objects in Data Streams
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Chapter title
Analyzing and Clustering Pareto-Optimal Objects in Data Streams
Chapter number 4
Book title
Learning from Data Streams in Evolving Environments
Published by
Springer, Cham, January 2019
DOI 10.1007/978-3-319-89803-2_4
Book ISBNs
978-3-31-989802-5, 978-3-31-989803-2
Authors

Markus Endres, Johannes Kastner, Lena Rudenko, Endres, Markus, Kastner, Johannes, Rudenko, Lena

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