Papers Binary Quantification
“Binary Quantification” 태그가 달린 논문 6편 · 필터 해제
Binary Quantification and Dataset Shift: An Experimental Investigation
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 data on which the predictor has …
Binary QuantificationContinuous Sweep for Binary Quantification Learning
A quantifier is a supervised machine learning algorithm, focused on estimating the class prevalence in a dataset rather than labeling its individual observations. We introduce Continuous Sweep, a new parametric binary qu…
Binary QuantificationFuzzy Rough Sets Based on Fuzzy Quantification
One of the weaknesses of classical (fuzzy) rough sets is their sensitivity to noise, which is particularly undesirable for machine learning applications. One approach to solve this issue is by making use of fuzzy quantif…
Binary QuantificationMulti-Label Quantification
Quantification, variously called "supervised prevalence estimation" or "learning to quantify", is the supervised learning task of generating predictors of the relative frequencies (a.k.a. "prevalence values") of the clas…
Binary QuantificationLeQua@CLEF2022: Learning to Quantify
LeQua 2022 is a new lab for the evaluation of methods for "learning to quantify" in textual datasets, i.e., for training predictors of the relative frequencies of the classes of interest in sets of unlabelled textual doc…
Binary QuantificationFormMulticlass QuantificationDoes quantification without adjustments work?
Classification is the task of predicting the class labels of objects based on the observation of their features. In contrast, quantification has been defined as the task of determining the prevalences of the different so…
Binary QuantificationGeneral Classification