In this paper, we propose a non-parametric Bayesian combination model to solve the problem of combining annotators' propositions when the label set is not initially known. In addition, we will show that a non-parametric model enables us to take more complex confusions for each annotator into account.
We propose a non-parametric Bayesian annotator combination model to solve the problem of learning the model when the labels set is not known as well as the problem of modeling complex confusions. We also discuss its relationship with the classical parametric model (described in [15], [23]). •. We develop variational ...
Feb 7, 2018 · • We propose a non-parametric Bayesian annotator combination model to solve the problem of learning the model when the labels set is not known as well as the problem of modelling complex confusions. We also discuss its relationship with the classical parametric model (described in [15, 23]). • We ...
We demonstrate improved estimation of RR is possible using our approach of fusing labels from different annotators, when compared with existing methods presented in the literature; namely two EM models by [1] and [2], as well as a hierarchical Gaussian process approach [8]. The remainder of this letter is organised as ...
Jul 18, 2014 · Abstract:Crowdsourcing has been proven to be an effective and efficient tool to annotate large datasets. User annotations are often noisy, so methods to combine the annotations to produce reliable estimates of the ground truth are necessary. We claim that considering the existence of clusters of ...
Two new fully unsupervised models based on a Chinese restaurant process (CRP) prior and a hierarchical structure that allows inferring these groups jointly with the ground truth and the properties of the users are proposed. Crowdsourcing has been proven to be an effective and efficient tool to annotate large ...
Non-parametric methods for estimating propensity scores and treatment effects are an attempt to remain agnostic about the precise functional relationships and avoid model mispecification. This push stems from the idea that excessive modelling assumptions cultivate an unlovely breeding ground for counfounding errors and ...
A non-parametric Bayesian model is proposed for processing multiple images. The analysis employs image features and, when present, the words associated with ... Each object is assumed to be represented as a heterogeneous mix of components, with this realized via mixture models linking image features to object types.
Missing: combination. | Show results with:combination.
Feb 23, 2021 · We propose to compare the performance of both parametric and non-parametric models and determine experimentally which method is more suitable for modelling physiological time-series data in the case when combining multiple imperfect algorithms to form a consensus, using the two public datasets as ...
Mar 29, 2024 · Bayesian nonparametric models offer a flexible and powerful frame- work for statistical model selection, enabling the adaptation of model complexity to the intricacies of diverse datasets. This survey intends to delve into the signif- icance of Bayesian nonparametrics, particularly in addressing complex ...