Extreme Learning Machine with Sigmoid Activation Function on Large Data
R. R. S. Ravi Kumar1, G. Apparao2

1R. R. S. Ravi Kumar, Department of CSE, GITAM, Visakhapatnam (Andhra Pradesh), India.
2Dr. G. Apparao, Department of CSE, GITAM, Visakhapatnam (Andhra Pradesh), India.
Manuscript received on 19 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 02 November 2019 | PP: 3523-3526 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B14330982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1433.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper describes an efficient algorithm for classification in large data set. While many algorithms exist for classification, they are not suitable for larger contents and different data sets. For working with large data sets various ELM algorithms are available in literature. However the existing algorithms using fixed activation function and it may lead deficiency in working with large data. In this paper, we proposed novel ELM comply with sigmoid activation function. The experimental evaluations demonstrate the our ELM-S algorithm is performing better than ELM,SVM and other state of art algorithms on large data sets.
Keywords: Machine Learning, Activation Function, Sigmoid, Classification.
Scope of the Article: Machine Learning