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An empirical evaluation of extreme learning machine: application to handwritten character recognition

Authors:

Dibyasundar Das,

Deepak Ranjan Nayak,

Banshidahar MajhiAuthors Info & Claims

Multimedia Tools and Applications, Volume 78, Issue 14

Pages 19495 - 19523

https://doi.org/10.1007/s11042-019-7330-0

Published: 01 July 2019 Publication History

Abstract

Extreme learning machine (ELM), a randomized learning paradigm for single hidden layer feed-forward network, has gained significant attention for solving problems in diverse domains due to its faster learning ability. The output weights in ELM are determined by an analytic procedure, while the input weights and biases are randomly generated and fixed during the training phase. The learning performance of ELM is highly sensitive to many factors such as the number of nodes in the hidden layer, the initialization of input weight and the type of activation functions in the hidden layer. Although various works on ELM have been proposed in the last decade, the effect of the all these influencing factors on classification performance has not been fully investigated yet. In this paper, we test the performance of ELM with different configurations through an empirical evaluation on three standard handwritten character datasets, namely, MNIST, ISI-Kolkata Bangla numeral, ISI-Kolkata Odia numeral and a newly developed NIT-RKL Bangla numeral dataset. Finally, we derive some best ELM figurations which can serve as general guidelines to design ELM based classifiers.

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Cited By

Dey RBalabantaray R(2023)Development of a Benchmark Odia Handwritten Character Database for an Efficient Offline Handwritten Character Recognition with a Chronological SurveyACM Transactions on Asian and Low-Resource Language Information Processing10.1145/358398822:6(1-28)Online publication date: 21-Feb-2023
https://dl.acm.org/doi/10.1145/3583988
Dey RBalabantaray RMohanty S(2022)Offline Odia handwritten character recognition with a focus on compound charactersMultimedia Tools and Applications10.1007/s11042-022-12148-z81:8(10469-10495)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s11042-022-12148-z
Dey RBalabantaray RMohanty S(2022)Sliding window based off-line handwritten text recognition using edit distanceMultimedia Tools and Applications10.1007/s11042-021-10988-981:16(22761-22788)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s11042-021-10988-9

Index Terms

An empirical evaluation of extreme learning machine: application to handwritten character recognition
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Index terms have been assigned to the content through auto-classification.

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cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 78, Issue 14

Jul 2019

1580 pages

ISSN:1380-7501

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Copyright © Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2019

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Cited By

Dey RBalabantaray R(2023)Development of a Benchmark Odia Handwritten Character Database for an Efficient Offline Handwritten Character Recognition with a Chronological SurveyACM Transactions on Asian and Low-Resource Language Information Processing10.1145/358398822:6(1-28)Online publication date: 21-Feb-2023
https://dl.acm.org/doi/10.1145/3583988
Dey RBalabantaray RMohanty S(2022)Offline Odia handwritten character recognition with a focus on compound charactersMultimedia Tools and Applications10.1007/s11042-022-12148-z81:8(10469-10495)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s11042-022-12148-z
Dey RBalabantaray RMohanty S(2022)Sliding window based off-line handwritten text recognition using edit distanceMultimedia Tools and Applications10.1007/s11042-021-10988-981:16(22761-22788)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s11042-021-10988-9

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