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Proceedings of ELM-2014 Volume 1

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Cover of 'Proceedings of ELM-2014 Volume 1'

Table of Contents

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    Book Overview
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    Chapter 1 Sparse Bayesian ELM Handling with Missing Data for Multi-class Classification
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    Chapter 2 A Fast Incremental Method Based on Regularized Extreme Learning Machine
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    Chapter 3 Parallel Ensemble of Online Sequential Extreme Learning Machine Based on MapReduce
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    Chapter 4 Explicit Computation of Input Weights in Extreme Learning Machines
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    Chapter 5 Subspace Detection on Concept Drifting Data Stream
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    Chapter 6 Inductive Bias for Semi-supervised Extreme Learning Machine
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    Chapter 7 ELM Based Efficient Probabilistic Threshold Query on Uncertain Data
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    Chapter 8 Sample-Based Extreme Learning Machine Regression with Absent Data
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    Chapter 9 Two Stages Query Processing Optimization Based on ELM in the Cloud
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    Chapter 10 Domain Adaptation Transfer Extreme Learning Machines
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    Chapter 11 Quasi-Linear Extreme Learning Machine Model Based Nonlinear System Identification
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    Chapter 12 A Novel Bio-inspired Image Recognition Network with Extreme Learning Machine
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    Chapter 13 A Deep and Stable Extreme Learning Approach for Classification and Regression
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    Chapter 14 Extreme Learning Machine Ensemble Classifier for Large-Scale Data
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    Chapter 15 Pruned Annular Extreme Learning Machine Optimization Based on RANSAC Multi Model Response Regularization
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    Chapter 16 Learning ELM Network Weights Using Linear Discriminant Analysis
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    Chapter 17 An Algorithm for Classification over Uncertain Data Based on Extreme Learning Machine
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    Chapter 18 Training Generalized Feedforword Kernelized Neural Networks on Very Large Datasets for Regression Using Minimal-Enclosing-Ball Approximation
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    Chapter 19 An Online Multiple-Model Approach to Univariate Time-Series Prediction
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    Chapter 20 A Self-Organizing Mixture Extreme Leaning Machine for Time Series Forecasting
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    Chapter 21 Ensemble Extreme Learning Machine Based on a New Self-adaptive AdaBoost.RT
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    Chapter 22 Machine Learning Reveals Different Brain Activities in Visual Pathway during TOVA Test
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    Chapter 23 Online Sequential Extreme Learning Machine with New Weight-Setting Strategy for Nonstationary Time Series Prediction
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    Chapter 24 RMSE-ELM: Recursive Model Based Selective Ensemble of Extreme Learning Machines for Robustness Improvement
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    Chapter 25 Extreme Learning Machine for Regression and Classification Using L 1 -Norm and L 2 -Norm
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    Chapter 26 A Semi-supervised Online Sequential Extreme Learning Machine Method
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    Chapter 27 ELM Feature Mappings Learning: Single-Hidden-Layer Feedforward Network without Output Weight
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    Chapter 28 ROS-ELM: A Robust Online Sequential Extreme Learning Machine for Big Data Analytics
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    Chapter 29 Deep Extreme Learning Machines for Classification
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    Chapter 30 C-ELM: A Curious Extreme Learning Machine for Classification Problems
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    Chapter 31 Review of Advances in Neural Networks: Neural Design Technology Stack
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    Chapter 32 Applying Regularization Least Squares Canonical Correlation Analysis in Extreme Learning Machine for Multi-label Classification Problems
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    Chapter 33 Least Squares Policy Iteration Based on Random Vector Basis
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    Chapter 34 Identifying Indistinguishable Classes in Multi-class Classification Data Sets Using ELM
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    Chapter 35 Effects of Training Datasets on Both the Extreme Learning Machine and Support Vector Machine for Target Audience Identification on Twitter
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    Chapter 36 Extreme Learning Machine for Clustering
Overall attention for this book and its chapters
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Mentioned by

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1 patent
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Title
Proceedings of ELM-2014 Volume 1
Published by
Springer International Publishing, December 2014
DOI 10.1007/978-3-319-14063-6
ISBNs
978-3-31-914062-9, 978-3-31-914063-6
Editors

Cao, Jiuwen, Mao, Kezhi, Cambria, Erik, Man, Zhihong, Toh, Kar-Ann

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 2%
Korea, Republic of 1 2%
Unknown 46 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 29%
Researcher 9 19%
Student > Master 7 15%
Student > Doctoral Student 4 8%
Student > Bachelor 4 8%
Other 6 13%
Unknown 4 8%
Readers by discipline Count As %
Computer Science 21 44%
Engineering 15 31%
Mathematics 1 2%
Linguistics 1 2%
Economics, Econometrics and Finance 1 2%
Other 3 6%
Unknown 6 13%