Density Ratio Estimation in Machine Learning

Stochastic Density Ratio Estimation and Its Application to Feature Selection

In this work, we deal with a relatively new statistical tool in machine learning: the estimation of the ratio of two probability densities, or density ratio estimation for short. As a side piece of research that gained its own traction, we also tackle the task of parameter selection in learning algorithms based on kernel methods. 1 Density Ratio Estimation The estimation of the ratio of two pro...

Density Ratio Estimation: A Comprehensive Review

Density ratio estimation has attracted a great deal of attention in the statistics and machine learning communities since it can be used for solving various statistical data processing tasks such as non-stationarity adaptation, two-sample test, outlier detection, independence test, feature selection/extraction, independent component analysis, causal inference, and conditional probability estima...

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Density Ratio Estimation: A New Versatile Tool for Machine Learning

A new general framework of statistical data processing based on the ratio of probability densities has been proposed recently and gathers a great deal of attention in the machine learning and data mining communities [1–17]. This density ratio framework includes various statistical data processing tasks such as non-stationarity adaptation [18, 1, 2, 4, 13], outlier detection [19–21, 6], and cond...

Machine learning algorithms in air quality modeling

Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...