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Learning in Non-Stationary Environments

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Cover of 'Learning in Non-Stationary Environments'

Table of Contents

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    Book Overview
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    Chapter 1 Prologue
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    Chapter 2 Incremental Statistical Measures
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    Chapter 3 A Granular Description of Data: A Study in Evolvable Systems
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    Chapter 4 Incremental Spectral Clustering
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    Chapter 5 Semisupervised Dynamic Fuzzy K -Nearest Neighbors
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    Chapter 6 Making Early Predictions of the Accuracy of Machine Learning Classifiers
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    Chapter 7 Incremental Classifier Fusion and Its Applications in Industrial Monitoring and Diagnostics
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    Chapter 8 Instance-Based Classification and Regression on Data Streams
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    Chapter 9 Flexible Evolving Fuzzy Inference Systems from Data Streams (FLEXFIS++)
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    Chapter 10 Sequential Adaptive Fuzzy Inference System for Function Approximation Problems
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    Chapter 11 Interval Approach for Evolving Granular System Modeling
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    Chapter 12 Dynamic Learning of Multiple Time Series in a Nonstationary Environment
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    Chapter 13 Optimizing Feature Calculation in Adaptive Machine Vision Systems
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    Chapter 14 Online Quality Control with Flexible Evolving Fuzzy Systems
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    Chapter 15 Identification of a Class of Hybrid Dynamic Systems
Attention for Chapter 13: Optimizing Feature Calculation in Adaptive Machine Vision Systems
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