Classification: From Expert to Ensemble to Crowds

SAITTA Lorenza

doi:10.3233/IA-140065

Classification: From Expert to Ensemble to Crowds

SAITTA Lorenza

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper a model of the classification task, based on the Monte Carlo theory of stochastic algorithms is presented. The model allows different classification paradigms to be described, such as the single classifier, the ensemble, and the crowdsourcing ones. Some experimental work validates the theoretical predictions about the relatioship between the mean and variance of the classification accuracy and the number of classifiers.

Original language	English
Pages (from-to)	3-22
Number of pages	20
Journal	Intelligenza Artificiale
Volume	8
DOIs	https://doi.org/10.3233/IA-140065
Publication status	Published - 1 Jan 2014
Externally published	Yes

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10.3233/IA-140065

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title = "Classification: From Expert to Ensemble to Crowds",

abstract = "In this paper a model of the classification task, based on the Monte Carlo theory of stochastic algorithms is presented. The model allows different classification paradigms to be described, such as the single classifier, the ensemble, and the crowdsourcing ones. Some experimental work validates the theoretical predictions about the relatioship between the mean and variance of the classification accuracy and the number of classifiers.",

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year = "2014",

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AB - In this paper a model of the classification task, based on the Monte Carlo theory of stochastic algorithms is presented. The model allows different classification paradigms to be described, such as the single classifier, the ensemble, and the crowdsourcing ones. Some experimental work validates the theoretical predictions about the relatioship between the mean and variance of the classification accuracy and the number of classifiers.

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DO - 10.3233/IA-140065

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JO - Intelligenza Artificiale

JF - Intelligenza Artificiale

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Classification: From Expert to Ensemble to Crowds

Abstract

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