SVM approach for non-parametric method in classification and regression learning process on feature selection with $epsilon$-insensitive region Article Swipe
M. Premalatha
,
C. Vijayalakshmi
,
C. Vijayalakshmi
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.26637/mjm0s01/0051
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.26637/mjm0s01/0051
Machine Learning is considered as a subfield of Artificial Intelligence.Machine Learning is concerned with the development of techniques and design the methods which enable the computer to learn.The field of machine learning is concerned with constructing computer program that automatically improve its performance with experience.In today's machine learning applications, support vector machines are considered (SVM) one of the most robust and accurate methods among all well-known algorithms and also being developed at a fast space.The aim of SVM is to find the best classification function, in a two-class learning task, and to distinguish between members of the two classes in the training data.Hence, the goal of machine learning is to find the output hypothesis that performed the correct classification of the training data, but the other earlier algorithms to find the hypothesis that accurate fit to the data.SVM requires that each data instance is represented as a vector of real numbers.Hence, if there are categorical attributes, convert them into numerical data, then we using m numbers to represent an m-category attribute.Only one of the m numbers is one, and others are zero.Machine learning has been applied in various field such as medical diagnosis, bioinformatics, detecting credit card fraud, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, and robot locomotion.The objective is algorithmic approach for non parametric methods to tractable for high dimensional massive datasets.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.26637/mjm0s01/0051
- http://malayajournal.org/articles/MJM0S010051.pdf
- OA Status
- diamond
- Cited By
- 3
- References
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2965861530
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https://openalex.org/W2965861530Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.26637/mjm0s01/0051Digital Object Identifier
- Title
-
SVM approach for non-parametric method in classification and regression learning process on feature selection with $epsilon$-insensitive regionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2019Year of publication
- Publication date
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2019-01-01Full publication date if available
- Authors
-
M. Premalatha, C. Vijayalakshmi, C. VijayalakshmiList of authors in order
- Landing page
-
https://doi.org/10.26637/mjm0s01/0051Publisher landing page
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https://malayajournal.org/articles/MJM0S010051.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://malayajournal.org/articles/MJM0S010051.pdfDirect OA link when available
- Concepts
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Feature selection, Support vector machine, Artificial intelligence, Selection (genetic algorithm), Regression, Pattern recognition (psychology), Parametric statistics, Process (computing), Machine learning, Computer science, Feature (linguistics), Mathematics, Statistics, Linguistics, Operating system, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1, 2022: 1, 2020: 1Per-year citation counts (last 5 years)
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11Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.numbers | 165, 175 |
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| abstract_inverted_index.machines | 51 |
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| abstract_inverted_index.concerned | 12, 33 |
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| abstract_inverted_index.represent | 167 |
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| abstract_inverted_index.sequences, | 200 |
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| abstract_inverted_index.algorithmic | 215 |
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| corresponding_author_ids | https://openalex.org/A5077725822 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I4399657946 |
| citation_normalized_percentile.value | 0.54142864 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |