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Progress in Artificial Intelligence
Erschienen in: Progress in Artificial Intelligence 3/2017

16.02.2017 | Regular Paper

The effect of human thought on data: an analysis of self-reported data in supervised learning and neural networks

verfasst von: Justin Lovinger, Iren Valova

Erschienen in: Progress in Artificial Intelligence | Ausgabe 3/2017

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Abstract

While scientific applications can gather consistent data from the natural world, psychological, sociological, and even economic applications rely on data provided by people. Since the majority of machine learning is aimed at improving the lives of people, human input is essential for useful results. In this paper, we explore datasets where input and target attributes are provided by people taking surveys. Every survey dataset, generated from human input, is reliable and self-consistent according to Cronbach’s alpha. One expects a reliable questionnaire to provide effective data for learning. It is this expectation that our analysis finds false, when applied to supervised learning. Both statistical analysis and application of several supervised learning architectures, with a focus on neural networks, are utilized to provide insight into data gathered through human input.
Vorheriger Artikel MR-DIS: democratic instance selection for big data by MapReduce
Nächster Artikel Preference stability along time: the time cohesiveness measure

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Metadaten
Titel
The effect of human thought on data: an analysis of self-reported data in supervised learning and neural networks
verfasst von
Justin Lovinger
Iren Valova
Publikationsdatum
16.02.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Progress in Artificial Intelligence / Ausgabe 3/2017
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI
https://doi.org/10.1007/s13748-017-0118-4

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