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Published in:

Volume 9 Issue 11
November-2022
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2211642


Registration ID:
506268

Page Number

g57-g64

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Title

Mathematical models for Machine Learning Techniques

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Abstract

The goal of the machine learning approaches is to automatically detect complex patterns in a given dataset, which enables inference or prediction to be made using those techniques on fresh datasets. The use of algorithms that are based on machine learning makes it possible to recognise similar groupings within an input dataset (unsupervised learning). Machine learning is a method for data analytics that trains computers to perform what comes naturally to both people and animals, which is to learn from experience. Algorithms that are used in machine learning make use of computational techniques in order to "learn" directly from data. These algorithms do not depend on a preconceived equation as a model. The number of samples that may be used for learning rises, and the algorithm is able to modify itself to get better results. Deep learning is a subfield of machine learning with its own unique set of challenges. A function is able to match input-output pairs via the process of supervised learning, which is an automated activity. A supervised learning algorithm's goal is to provide a function that maps the input-output (vector-supervision signal) pair as its output. This function can then be used for further learning. The technique makes it possible, in the best possible case, to accurately label the data in order to establish the classes. In the related field of human psychology, this phenomenon is referred to as conceptual learning. The Convolutional Neural Network (CNN), Support Vector Machines (SVM), Logistic Regression (LR), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Decision Trees (DT), and Random Forest (RF) supervised learning algorithms and associated mathematical models were investigated in this research.

Key Words

Machine Learning Approaches, Machine Learning Techniques, supervised learning algorithms, Support Vector Machines (SVM), Logistic Regression (LR), Naive Bayes (NB), Linear Discriminant Analysis (LDA),

Cite This Article

"Mathematical models for Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 11, page no.g57-g64, November-2022, Available :http://www.jetir.org/papers/JETIR2211642.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Mathematical models for Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 11, page no. ppg57-g64, November-2022, Available at : http://www.jetir.org/papers/JETIR2211642.pdf

Publication Details

Published Paper ID: JETIR2211642
Registration ID: 506268
Published In: Volume 9 | Issue 11 | Year November-2022
DOI (Digital Object Identifier):
Page No: g57-g64
Country: Delhi, Delhi, India .
Area: Mathematics
ISSN Number: 2349-5162
Publisher: IJ Publication


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