Quantification (or prevalence estimation) algorithms aim at predicting the class distribution of
unseen sets (or bags) of examples. These methods are useful for two main tasks: 1) …

This open access book provides an introduction and an overview of learning to quantify (aka
“quantification”), ie the task of training estimators of class proportions in unlabeled data by …

Sentiment quantification is the task of training, by means of supervised learning, estimators
of the relative frequency (also called “prevalence”) of sentiment-related classes (such as …

Learning to quantify (aka quantification) is a task concerned with training unbiased
estimators of class prevalence via supervised learning. This task originated with the …

Sentiment analysis has been one of the most active research areas in the past decade due
to its vast applications. Sentiment quantification, a new research problem in this field …

For the binary prevalence quantification problem under prior probability shift, we determine
the asymptotic variance of the maximum likelihood estimator. We find that it is a function of …

Quantification, variously called supervised prevalence estimation or learning to quantify, is
the supervised learning task of generating predictors of the relative frequencies (aka …

Quantification is a research area that develops methods that estimate the class attribute
prevalence in an independent sample. Like the other fields in Machine Learning …

Class Prior Estimation under Covariate Shift: No Problem? Page 1 Class Prior Estimation under
Covariate Shift: No Problem? Dirk Tasche Independent Researcher1 September 23, 2022 1 …

Quantification is the supervised learning task that consists of training predictors of the class
prevalence values of sets of unlabelled data, and is of special interest when the labelled …