Reciprocal Human-Machine Learning: A Theory and an Instantiation for the Case of Message Classification

Dov Te'eni; Inbal Yahav; Alexely Zagalsky; David Schwartz; Gahl Silverman; Daniel Cohen; Yossi Mann; Dafna Lewinsky

doi:10.1287/mnsc.2022.03518

Reciprocal Human-Machine Learning: A Theory and an Instantiation for the Case of Message Classification

Dov Te'eni, Inbal Yahav, Alexely Zagalsky, David Schwartz, Gahl Silverman, Daniel Cohen, Yossi Mann, Dafna Lewinsky

Coller School of Management

Tel Aviv University

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

Original language	English
Number of pages	27
Journal	Management Science
DOIs	https://doi.org/10.1287/mnsc.2022.03518
State	Published Online - 14 Nov 2023

Keywords

Design science
Human-machine interaction
Reciprocal learning

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10.1287/mnsc.2022.03518

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keywords = "Design science, Human-machine interaction, Reciprocal learning",

author = "Dov Te'eni and Inbal Yahav and Alexely Zagalsky and David Schwartz and Gahl Silverman and Daniel Cohen and Yossi Mann and Dafna Lewinsky",

year = "2023",

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doi = "10.1287/mnsc.2022.03518",

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T2 - A Theory and an Instantiation for the Case of Message Classification

AU - Te'eni, Dov

AU - Yahav, Inbal

AU - Zagalsky, Alexely

AU - Schwartz, David

AU - Silverman, Gahl

AU - Cohen, Daniel

AU - Mann, Yossi

AU - Lewinsky, Dafna

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KW - Human-machine interaction

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DO - 10.1287/mnsc.2022.03518

M3 - مقالة

SN - 0025-1909

JO - Management Science

JF - Management Science

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Reciprocal Human-Machine Learning: A Theory and an Instantiation for the Case of Message Classification

Keywords

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