Design of Improved Version of Sigmoidal Function with Biases for Classification Task in ELM Domain

Author:

S. R. Mugunthan,T. Vijayakumar

Abstract

Extreme Learning Machine (ELM) is one of the latest trends in learning algorithm, which can provide a good recognition rate within less computation time. Therefore, the algorithm can sustain for a faster response application by utilizing a feed-forward neural network. In this research article, the ELM method has been designed with the presence of sigmoidal function of biases in the hidden nodes to perform the classification task. The classification task is very challenging with the existing learning algorithm and increased computation time due to the huge amount of dataset. While handling of the stochastic matrix for hidden layer, output provides the lower performance for learning rate and robustness in the determination. To address these issues, the modified version of ELM has been developed to obtain better accuracy and minimize the classification error. This research article includes the mathematical proof of sigmoidal activation function with biases of the hidden nodes present in the networks. The output matrix maintains the column rank in order to improve the speed of the training output weights (β). The proposed improved version of ELM leverages better accuracy and efficacy in classification and regression problems as well. Due to the inclusion of matrix column ranking in mathematical proof, the proposed improved version of ELM solves the slow training speed and over-fitting problems present in the existing learning approach.

Publisher

Inventive Research Organization

Cited by 60 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Evaluating Apparent Temperature in the Contiguous United States From Four Reanalysis Products Using Artificial Neural Networks;Journal of Geophysical Research: Machine Learning and Computation;2024-05-14

2. Design of an Intelligent Network English Automatic Scoring Algorithm based on CS-ELM;2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT);2024-03-15

3. Extreme-learning-machine-based architecture to assess the reliability of the integrated energy systems;2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP);2023-04-21

4. Social Media Mining to Detect Mental Health Disorders Using Machine Learning;Advances in Intelligent Systems and Computing;2023

5. Deep Learning Techniques for Dental Image Diagnostics: A Survey;2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS);2022-11-24

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3