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15370-68251-1-PB.pdf
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🔗 Source: cdn.aaai.org
📊 Size: 723 KB
👤 Author: Francisco, Bernardo; Michael, Zbyszynski; Rebecca, Fiebrink; Mick, Grierson;
⬇️ Downloads: 359

Summary

Interactive machine learning (IML) enables end-users to create and customize systems by iteratively training and refining machine learning models. IML supports user-centered design processes and can improve user experiences by allowing users to provide training examples and adjust model parameters. This approach can be used to create customized systems, such as computer vision classifiers, sensor-based interactions, and gestural controllers. IML can be understood as a tool for end-user programming and can democratize machine learning, making it accessible to a wider range of people. The benefits of IML include rapid prototyping, iterative refinement, and direct communication of desired system behavior. However, challenges include difficulty in reasoning about system performance and the need for effective user interfaces. A User Innovation Toolkit is being developed to support end-user innovation with IML, and the RAPID-MIX project aims to improve user experiences for both end-users and professional developers using machine learning to build new interactive systems.

Description

Interactive machine learning (IML) enables end-users to create and customize systems by iteratively training and refining machine learning models.

Technical Information

  • File Format: PDF
  • File Size: 723 KB
  • Pages: 7
  • Language: EN
  • Author: Francisco, Bernardo; Michael, Zbyszynski; Rebecca, Fiebrink; Mick, Grierson;
  • Total Downloads: 359
  • Last Updated: 7 hours ago

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