| Autor: |
Negi, Harendra Singh, Dimri, Sushil Chandra, Kumar, Bhawnesh, Ram, Mangey |
| Předmět: |
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| Zdroj: |
Mathematics in Engineering, Science & Aerospace (MESA); 2024, Vol. 15 Issue 2, p401-409, 9p |
| Abstrakt: |
A great deal of study has been done recently on support vector machines (SVMs) and how they are used in various scientific domains. The statistical learning theory's mathematical foundation allows SVM to offer logical solutions to issues. A portion of the training input serves as the SVM's solution. SVM is frequently employed in applications involving feature reduction. regression. and novelty detection. Additionally, studies conducted in certain fields where SVM performs badly have prompted the creation of alternative SVM applications. including big data sets, multiclassification SVM. and imbalanced data sets SVM. Furthermore, SVM is used with other more sophisticated techniques. such as evolve algorithms. to enhance classification performance and optimize parameters. SVM algorithms are now widely used in research and applications across a number of scientific and engineering domains. The SVM might be one of the primary options for big data compatibility and big data classification because of its advantages. Data preparation methods must be created in order to mask data into the appropriate format for learning in order to do this. This paper provides a quick overview of SVM. list several applications, draw attention to current issues and patterns, and point out sonic of SVM's drawbacks in this essay. This document can be used to categorize vast volumes of data and recommend research areas for further investigation. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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