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Proceedings of ELM-2014 Volume 1

Algorithms and Theories

  • Conference proceedings
  • © 2015

Overview

Editors:
  1. Jiuwen Cao

    1. Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China

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  2. Kezhi Mao

    1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

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  3. Erik Cambria

    1. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

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  4. Zhihong Man

    1. Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, Hawthorn, Australia

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  5. Kar-Ann Toh

    1. School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of (South Korea)

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  • Recent research on Extreme Learning Machines
  • Results of the International Conference on Extreme Learning Machines (ELM-2014) held at Marina Bay Sands, Singapore, December 8-10, 2014
  • Presents Theory, Algorithms and Applications

Part of the book series: Proceedings in Adaptation, Learning and Optimization (PALO, volume 3)

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Table of contents (36 papers)

  1. Front Matter

    Pages 1-8
    Download chapter PDF

  2. Sparse Bayesian ELM Handling with Missing Data for Multi-class Classification

    • Jiannan Zhang, Shiji Song, Xunan Zhang
    Pages 1-13

  3. A Fast Incremental Method Based on Regularized Extreme Learning Machine

    • Zhixin Xu, Min Yao
    Pages 15-30

  4. Parallel Ensemble of Online Sequential Extreme Learning Machine Based on MapReduce

    • Shan Huang, Botao Wang, Junhao Qiu, Jitao Yao, Guoren Wang, Ge Yu
    Pages 31-40

  5. Explicit Computation of Input Weights in Extreme Learning Machines

    • Jonathan Tapson, Philip de Chazal, André van Schaik
    Pages 41-49

  6. Subspace Detection on Concept Drifting Data Stream

    • Lin Feng, Shenglan Liu, Yao Xiao, Jing Wang
    Pages 51-59

  7. Inductive Bias for Semi-supervised Extreme Learning Machine

    • Federica Bisio, Sergio Decherchi, Paolo Gastaldo, Rodolfo Zunino
    Pages 61-70

  8. ELM Based Efficient Probabilistic Threshold Query on Uncertain Data

    • Jiajia Li, Botao Wang, Guoren Wang
    Pages 71-80

  9. Sample-Based Extreme Learning Machine Regression with Absent Data

    • Hang Gao, Xinwang Liu, Yuxing Peng
    Pages 81-90

  10. Two Stages Query Processing Optimization Based on ELM in the Cloud

    • Linlin Ding, Yu Liu, Baoyan Song, Junchang Xin
    Pages 91-102

  11. Domain Adaptation Transfer Extreme Learning Machines

    • Lei Zhang, David Zhang
    Pages 103-119

  12. Quasi-Linear Extreme Learning Machine Model Based Nonlinear System Identification

    • Dazi Li, Qianwen Xie, Qibing Jin
    Pages 121-130

  13. A Novel Bio-inspired Image Recognition Network with Extreme Learning Machine

    • Lin Zhang, Yu Zhang, Ping Li
    Pages 131-139

  14. A Deep and Stable Extreme Learning Approach for Classification and Regression

    • Le-le Cao, Wen-bing Huang, Fu-chun Sun
    Pages 141-150

  15. Extreme Learning Machine Ensemble Classifier for Large-Scale Data

    • Haocheng Wang, Qing He, Tianfeng Shang, Fuzhen Zhuang, Zhongzhi Shi
    Pages 151-161

  16. Pruned Annular Extreme Learning Machine Optimization Based on RANSAC Multi Model Response Regularization

    • Lavneet Singh, Girija Chetty
    Pages 163-182

  17. Learning ELM Network Weights Using Linear Discriminant Analysis

    • Philip de Chazal, Jonathan Tapson, André van Schaik
    Pages 183-191

  18. An Algorithm for Classification over Uncertain Data Based on Extreme Learning Machine

    • Ke-yan Cao, Guoren Wang, Donghong Han
    Pages 193-202

  19. Training Generalized Feedforword Kernelized Neural Networks on Very Large Datasets for Regression Using Minimal-Enclosing-Ball Approximation

    • Jun Wang, Zhaohong Deng, Shitong Wang, Qun Gao
    Pages 203-214

  20. An Online Multiple-Model Approach to Univariate Time-Series Prediction

    • Koshy George, Sachin Prabhu, Prabhanjan Mutalik
    Pages 215-224
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Keywords

About this book

This book contains some selected papers from the International Conference on Extreme Learning Machine 2014, which was held in Singapore, December 8-10, 2014. This conference brought together the researchers and practitioners of Extreme Learning Machine (ELM) from a variety of fields to promote research and development of “learning without iterative tuning”.  The book covers theories, algorithms and applications of ELM. It gives the readers a glance of the most recent advances of ELM.

Editors and Affiliations

  • Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  • School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kezhi Mao

  • School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria

  • Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, Hawthorn, Australia

    Zhihong Man

  • School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of (South Korea)

    Kar-Ann Toh

Bibliographic Information

  • Book Title: Proceedings of ELM-2014 Volume 1

  • Book Subtitle: Algorithms and Theories

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

  • Series Title: Proceedings in Adaptation, Learning and Optimization

  • DOI: https://doi.org/10.1007/978-3-319-14063-6

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2015

  • Hardcover ISBN: 978-3-319-14062-9Published: 29 December 2014

  • Softcover ISBN: 978-3-319-36684-5Published: 23 August 2016

  • eBook ISBN: 978-3-319-14063-6Published: 04 December 2014

  • Series ISSN: 2363-6084

  • Series E-ISSN: 2363-6092

  • Edition Number: 1

  • Number of Pages: VIII, 446

  • Number of Illustrations: 124 b/w illustrations

  • Topics: Computational Intelligence, Artificial Intelligence

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