Gabor Regression

TL;DR

Regression approach that models continuous targets using Gabor functions rather than simple linear or polynomial bases.
Captures both frequency and time-domain characteristics of signals, making it suitable for complex, nonlinear patterns.
Applied to prediction problems where signal-like structure is present (for example, audio or other time-varying data).

Definition

Gabor regression is a type of machine learning algorithm used for modeling and predicting continuous values. It is based on Gabor functions, which are mathematical functions used to represent signals in a way similar to how the human auditory system processes sound.

Explanation

Gabor regression performs regression by using Gabor functions to model the data instead of simple linear or polynomial functions. Like other regression methods (for example, linear regression or support vector regression), it finds a best-fit model for given data points, but its use of Gabor functions allows it to represent both frequency and time-domain characteristics of a signal. This makes Gabor regression capable of modeling signals with complex, nonlinear patterns, such as speech or other audio signals.

Examples

Predicting the price of a stock

Gabor regression could be used to model historical price data of a stock. By using Gabor functions to capture the complex, nonlinear patterns in the data, the algorithm can then be used to make predictions about the future price of the stock.

Modeling the relationship between weather and crop yield

Gabor regression could be used to model the relationship between weather variables, such as temperature and precipitation, and the yield of a crop. By using Gabor functions to capture the complex, nonlinear patterns in the data, the algorithm can then be used to make predictions about the yield of a crop given certain weather conditions.

Linear regression
Support vector regression