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Regression and Classification in Supervised Learning

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Jiachong LiAuthors Info & Claims

ICCBD '19: Proceedings of the 2nd International Conference on Computing and Big Data

Pages 99 - 104

https://doi.org/10.1145/3366650.3366675

Published: 18 October 2019 Publication History

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Abstract

The problem of recognizing patterns from big data has attracted a lot of attention these days, especially in artificial intelligence and machine learning fields. People are interested in training computers to make predictions or classifications on their own based on past experience, i.e., data. In this paper, we review three fundamental supervised learning models (linear regression, logistic regression, and perceptron) for both regression and classification tasks, including their theoretical background, algorithmic solutions, and application scenarios. We also conduct synthetic experiments to demonstrate their performance.

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Gadhafi RCopiaco AHimeur YAfsari KMukhtar HGhanem KMansoor W(2024)Exploring the Potential of Deep-Learning and Machine-Learning in Dual-Band Antenna DesignIEEE Open Journal of the Computer Society10.1109/OJCS.2024.34631905(566-577)Online publication date: 2024
https://doi.org/10.1109/OJCS.2024.3463190
Benvenuti EBierhalz AFries CSteffens F(2024)Efficiency analysis in knitted fabric portfolio management using DEAResearch Journal of Textile and Apparel10.1108/RJTA-08-2023-0088Online publication date: 18-Jul-2024
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Es-haghi MAnitescu CRabczuk T(2024)Methods for enabling real-time analysis in digital twinsComputers and Structures10.1016/j.compstruc.2024.107342297:COnline publication date: 9-Jul-2024
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Index Terms

Regression and Classification in Supervised Learning
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
      1. Continuous optimization
        Linear programming

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Published In

ICCBD '19: Proceedings of the 2nd International Conference on Computing and Big Data

October 2019

173 pages

ISBN:9781450372909

DOI:10.1145/3366650

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 18 October 2019

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ICCBD 2019: 2019 the 2nd International Conference on Computing and Big Data

October 18 - 20, 2019

Taichung, Taiwan

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Gadhafi RCopiaco AHimeur YAfsari KMukhtar HGhanem KMansoor W(2024)Exploring the Potential of Deep-Learning and Machine-Learning in Dual-Band Antenna DesignIEEE Open Journal of the Computer Society10.1109/OJCS.2024.34631905(566-577)Online publication date: 2024
https://doi.org/10.1109/OJCS.2024.3463190
Benvenuti EBierhalz AFries CSteffens F(2024)Efficiency analysis in knitted fabric portfolio management using DEAResearch Journal of Textile and Apparel10.1108/RJTA-08-2023-0088Online publication date: 18-Jul-2024
https://doi.org/10.1108/RJTA-08-2023-0088
Es-haghi MAnitescu CRabczuk T(2024)Methods for enabling real-time analysis in digital twinsComputers and Structures10.1016/j.compstruc.2024.107342297:COnline publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1016/j.compstruc.2024.107342
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Logistic Regression Modeling for Context-Based Classification

Logistic regression diagnostics in ridge regression

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