Neural Network Machine Learning Analysis for Noisy Data: R Programming
By: Rimal, Yagyanath.
Publisher: New Delhi STM Journals 2019Edition: Vol.6(3), Sep-Dec.Description: 1-10p.Subject(s): Computer EngineeringOnline resources: Click here In: Recent trends in programming languagesSummary: 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.Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database | School of Engineering & Technology Archieval Section | Not for loan | 2021094 |
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.
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