We present a noise resilient probabilistic model for ac-tive learning of a Gaussian process classifier from crowds, i.e., a set of noisy labelers. It explicitly models both the overall label noises and the expertise level of each individ-ual labeler in two levels of flip models. Expectation propa-gation is adopted for efficient approximate Bayesian infer-ence of our probabilistic model for classification, based on which, a generalized EM algorithm is derived to estimate both the global label noise and the expertise of each indi-vidual labeler. The probabilistic nature of our model im-mediately allows the adoption of the prediction entropy and estimated expertise for active selection of data sample to be labeled, and active selection of high...
Crowdsourcing platforms offer a practical solution to the problem of afford-ably annotating large da...
International audienceIn machine learning, training a classifier on large dataset requires an import...
Classification is a fundamental topic in the literature of data mining and all recent hot topics lik...
Abstract We present a noise resilient probabilistic model for active learning of a Gaussian process ...
Although supervised learning requires a labeled dataset, obtaining labels from experts is generally ...
Although supervised learning requires a labeled dataset, ob- taining labels from experts is generall...
With the proliferation of social media, gathering data has became cheaper and easier than before. Ho...
This paper studies the active learning problem in crowdsourcing settings, where multiple imperfect a...
Biased labelers are a systemic problem in crowdsourcing, and a comprehensive toolbox for handling th...
Discriminative methods for visual object category recognition are typically non-probabilistic, predi...
In this paper a model of the classification task, based on the Monte Carlo theory of stochastic algo...
With crowdsourcing systems, labels can be obtained with low cost, which facilitates the creation of ...
Modern machine learning-based approaches to computer vision require very large databases of hand lab...
Active learning is an effective way to relieve the tedious work of manual annotation in many applica...
To deal with the low qualities of web workers in crowdsourcing, many unsupervised label aggregation ...
Crowdsourcing platforms offer a practical solution to the problem of afford-ably annotating large da...
International audienceIn machine learning, training a classifier on large dataset requires an import...
Classification is a fundamental topic in the literature of data mining and all recent hot topics lik...
Abstract We present a noise resilient probabilistic model for active learning of a Gaussian process ...
Although supervised learning requires a labeled dataset, obtaining labels from experts is generally ...
Although supervised learning requires a labeled dataset, ob- taining labels from experts is generall...
With the proliferation of social media, gathering data has became cheaper and easier than before. Ho...
This paper studies the active learning problem in crowdsourcing settings, where multiple imperfect a...
Biased labelers are a systemic problem in crowdsourcing, and a comprehensive toolbox for handling th...
Discriminative methods for visual object category recognition are typically non-probabilistic, predi...
In this paper a model of the classification task, based on the Monte Carlo theory of stochastic algo...
With crowdsourcing systems, labels can be obtained with low cost, which facilitates the creation of ...
Modern machine learning-based approaches to computer vision require very large databases of hand lab...
Active learning is an effective way to relieve the tedious work of manual annotation in many applica...
To deal with the low qualities of web workers in crowdsourcing, many unsupervised label aggregation ...
Crowdsourcing platforms offer a practical solution to the problem of afford-ably annotating large da...
International audienceIn machine learning, training a classifier on large dataset requires an import...
Classification is a fundamental topic in the literature of data mining and all recent hot topics lik...