AI-Café presents: Exit the Needle, Enter the Haystack: Supervised Machine Learning for Aggregate Data
May 31st, 2023 (from 2PM to 3PM CEST)
Description of the Talk:
Learning to quantify (a.k.a. “quantification", or "class prior estimation”) is the task of using supervised learning for training “quantifiers”, i.e., estimators of class proportions in unlabelled data. In data science, learning to quantify is a task of its own, related to classification yet different from it, since estimating class proportions by simply classifying all data and counting the labels assigned by the classifier (the “classify and count” method) is known to often return inaccurate class proportion estimates. In this talk I will introduce learning to quantify by discussing applications of learning to quantify, by looking at the reasons why “classify and count” is a suboptimal quantification method, by illustrating some better quantification methods, and by discussing open problems in quantification research.
Wacth the Recording:
Speaker:
Fabrizio Sebastiani (Institute for the Science and Technology of Information Italian National Council of Research)
Speaker's short bio:
All the recordings of past AI-Cafés are available on this YouTube channel.
AI-Café Team
Carmen Mac Williams Organizer, and Moderator of the AI-Cafe. She is the Director of the company Grassroots Arts, and a partner in the European AI4media project.
Emma, co-organiser and co-moderator of the AI-Cafés. Research Assistant at Grassroots Arts.
This Café is organized by Grassroots Arts. If you have questions about the organisation of this AI-Café or if you want to become a Speaker yourself in one of the next Web Cafe Sessions, please contact carmen@grassroots-arts.eu.
The recordings of the past Web Cafes you can find on our AI-Café video channel: https://www.gotostage.com/channel/ai-cafe. Here is the link to the AI-Cafe website: https://ai-cafe.eu/
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