Regression

• Regression is a statistical and machine learning concept that may refer to:
  • Supervised Learning Task.
  • Regression Analysis Task
• See: Supervised Point Estimation Task, Segmented Regression, Regression Tree, Simple Linear Regression Algorithm, Linear Regression, Logistic Regression, Least Squares, Least-Angle Regression Algorithm, Nonlinear Regression, Nonparametric Regression, Robust Regression, Stepwise Regression.

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References

2017

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Regression#Statistics Retrieved:2017-7-23.
  • Regression analysis, a statistical technique for estimating the relationships among variables. There are several types of regression:
    • Linear regression
    • Simple linear regression
    • Logistic regression
    • Nonlinear regression
    • Nonparametric regression
    • Robust regression
    • Stepwise regression
    • Regression toward the mean, a common statistical phenomenon

2011A

• (Quadrianto & Buntine, 2011) ⇒ Novi Quadrianto and Wray L. Buntine (2011). "Regression" In: (Sammut & Webb, 2011) pg. 1075-1080
  • QUOTE: Regression is a fundamental problem in statistics and machine learning. In regression studies, we are typically interested in inferring a real-valued function (called a regression function) whose values correspond to the mean of a dependent (or response or output) variable conditioned on one or more independent (or input) variables. Many different techniques for estimating this regression function have been developed, including parametric, semi-parametric, and nonparametric methods.

2011B

• (Sebag, 2011) ⇒ Michele Sebag (2011). "Nonstandard Criteria in Evolutionary Learning". In: Encyclopedia of Machine Learning (Sammut & Webb, 2011, pp. 722-731). Springer US.
  • QUOTE: Machine learning (ML), primarily concerned with extracting models or hypotheses from data, comes into three main flavors: supervised learning also known as classification or regression (Bishop 2006; Duda et al. 2001; Han and Kamber 2000), unsupervised learning also known as clustering (Ben-David et al. 2005), and reinforcement learning (Sutton and Barto 1998).

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