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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 11: Efficient Estimation of Dynamic Density Functions with Applications in Data Streams
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Chapter title
Efficient Estimation of Dynamic Density Functions with Applications in Data Streams
Chapter number 11
Book title
Learning from Data Streams in Evolving Environments
Published by
Springer, Cham, January 2019
DOI 10.1007/978-3-319-89803-2_11
Book ISBNs
978-3-31-989802-5, 978-3-31-989803-2
Authors

Abdulhakim Qahtan, Suojin Wang, Xiangliang Zhang, Qahtan, Abdulhakim, Wang, Suojin, Zhang, Xiangliang

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Computer Science 1 100%

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