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pro vyhledávání: '"MOREO, ALEJANDRO"'
Quantification, also known as class prevalence estimation, is the supervised learning task in which a model is trained to predict the prevalence of each class in a given bag of examples. This paper investigates the application of deep neural networks
Externí odkaz:
http://arxiv.org/abs/2403.15123
Autor:
Corbara, Silvia, Moreo, Alejandro
Authorship Verification (AV) is a text classification task concerned with inferring whether a candidate text has been written by one specific author or by someone else. It has been shown that many AV systems are vulnerable to adversarial attacks, whe
Externí odkaz:
http://arxiv.org/abs/2403.11265
Several disciplines, like the social sciences, epidemiology, sentiment analysis, or market research, are interested in knowing the distribution of the classes in a population rather than the individual labels of the members thereof. Quantification is
Externí odkaz:
http://arxiv.org/abs/2401.00490
This open access book provides an introduction and an overview of learning to quantify (a.k.a. “quantification”), i.e. the task of training estimators of class proportions in unlabeled data by means of supervised learning. In data science, learni
Externí odkaz:
https://library.oapen.org/handle/20.500.12657/62385
While a substantial amount of work has recently been devoted to enhance the performance of computational Authorship Identification (AId) systems, little to no attention has been paid to endowing AId systems with the ability to explain the reasons beh
Externí odkaz:
http://arxiv.org/abs/2311.02237
Quantification, i.e., the task of training predictors of the class prevalence values in sets of unlabeled data items, has received increased attention in recent years. However, most quantification research has concentrated on developing algorithms fo
Externí odkaz:
http://arxiv.org/abs/2310.09210
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 been trained and the unlabelle
Externí odkaz:
http://arxiv.org/abs/2310.04565
We investigate the effects on authorship identification tasks of a fundamental shift in how to conceive the vectorial representations of documents that are given as input to a supervised learner. In ``classic'' authorship analysis a feature vector re
Externí odkaz:
http://arxiv.org/abs/2301.09862
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 classes of interest in unlabelled
Externí odkaz:
http://arxiv.org/abs/2211.08063
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 documents. While these prediction
Externí odkaz:
http://arxiv.org/abs/2111.11249