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Definition

(Für diese Definition ist die deutsche Übersetzung noch nicht abgeschlossen)

Techniques for image or video recognition or understanding using regression

Notes – technical background

These notes provide more information about the technical subject matter that is classified in this place:

The term “regression” refers to statistical techniques for estimating the relationships between a dependent variable (often called the “outcome” or “response” variable) and one or more independent variables (often called “predictors”, “covariates” or “explanatory variables”), where the variables model the underlying image or video data.

Common forms of regression are:

• Linear regression - the model specification is that the dependent variables are a linear combination of the parameters (but need not be linear in the independent variables); The goal is to find a line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion (e.g. by minimising the least-mean-squares criterion). For example, the method of ordinary least squares computes the unique line (or hyperplane) that minimises the sum of squared differences between the true data and that line (or hyperplane);
• Non-linear regression, e.g. polynomial, binomial, binary, logistic, multinomial logistic, etc.

Examples

Example of adaptive regression analysis for classification

Querverweise

Informative Querverweise

Glossar