Neural Network Machine Learning Analysis for Noisy Data: R Programming

Authors

  • Yagyanath Rimal

DOI:

https://doi.org/10.37591/rtpl.v6i3.2324

Abstract

Abstract: This review paper clearly discusses the compression between Neural Network Machine Learning Analysis for Noisy Data: R Programming. Although there is large gap between data analysis to analyze overfitting and multicollinearity problems in data sets. Its primary purpose is to explain the machine learning procedures using neural network whose data structure were cross validation using R software whose outputs were sufficiently explain with various intermediate output and graphical interpretation to reach the conclusion. Therefore, this paper presents easiest way of machine learning analysis when data sets with multicollinearity and its strengths for data analysis using R programming.

Keyword: Hidden Neuron, Neural Network, over fitted Data, Rectified Linear Unit, Multi-Layer Perceptron

Cite this Article: Yagyanath Rimal. Neural Network Machine Learning Analysis for Noisy Data: R Programming. Recent Trends in Programming Languages. 2019; 6(3): 1–10p.

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Published

2019-11-30

Issue

Vol. 6 No. 3 (2019)

Section

Research Articles

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