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Proceedings of ELM2019

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Cover of 'Proceedings of ELM2019'

Table of Contents

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    Book Overview
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    Chapter 1 Evolutionary Extreme Learning Machine Weighted Fuzzy-Rough Nearest-Neighbour Classification
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    Chapter 2 NNRW-Based Algorithm Selection for Software Model Checking
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    Chapter 3 An Extreme Learning Machine Method for Diagnosis of Patellofemoral Pain Syndrome
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    Chapter 4 Extreme Learning Machines for Signature Verification
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    Chapter 5 Website Classification from Webpage Renders
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    Chapter 6 ELM Algorithm Optimized by WOA for Motor Imagery Classification
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    Chapter 7 The Octonion Extreme Learning Machine
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    Chapter 8 Scikit-ELM: An Extreme Learning Machine Toolbox for Dynamic and Scalable Learning
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    Chapter 9 High-Performance ELM for Memory Constrained Edge Computing Devices with Metal Performance Shaders
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    Chapter 10 Validating Untrained Human Annotations Using Extreme Learning Machines
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    Chapter 11 ELM Feature Selection and SOM Data Visualization for Nursing Survey Datasets
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    Chapter 12 Application of Extreme Learning Machine to Knock Probability Control of SI Combustion Engines
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    Chapter 13 Extreme Learning Machine for Multilayer Perceptron Based on Multi-swarm Particle Swarm Optimization for Variable Topology
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    Chapter 14 Investigating Feasibility of Active Learning with Image Content on Mobile Devices Using ELM
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    Chapter 15 The Modeling of Decomposable Gene Regulatory Network Using US-ELM
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    Chapter 16 Multi-level Cascading Extreme Learning Machine and Its Application to CSI Based Device-Free Localization
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    Chapter 17 A Power Grid Cascading Failure Model Considering the Line Vulnerability Index
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    Chapter 18 Extreme Learning Machines Classification of Kick Gesture
Attention for Chapter 12: Application of Extreme Learning Machine to Knock Probability Control of SI Combustion Engines
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Chapter title
Application of Extreme Learning Machine to Knock Probability Control of SI Combustion Engines
Chapter number 12
Book title
Proceedings of ELM2019
Published by
Springer, Cham, December 2019
DOI 10.1007/978-3-030-58989-9_12
Book ISBNs
978-3-03-058988-2, 978-3-03-058989-9
Authors

Kai Zhao, Tielong Shen, Zhao, Kai, Shen, Tielong

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 > Doctoral Student 1 100%
Readers by discipline Count As %
Engineering 1 100%