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Machine Learning and Explainable AI Lab (Akihiro Yamaguchi)
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The lab works on making AI that classifies time series data interpretable.
AboutAbout the lab
Research on explainable AI and time series data.
Prof. Yamaguchi lists explainable AI (XAI), interpretability, machine learning, and time series data mining as research topics.
Prof. Yamaguchi is a Professor in the Liaison Strategy Division at the Institute of Mathematics for Industry, Kyushu University.
In the papers listed in the researcher profile, the work deals with AI that classifies time series data, and with methods that let people interpret trained classifiers.
Key terms
- Explainable AI (XAI)
- Technology that lets people interpret trained classifiers. It is needed in industrial fields that require high reliability and safety
- Time series data
- Data arranged in order of time. With the spread of IoT, technology for classifying data into classes with AI is becoming increasingly important
ResearchResearch themes
Prof. Yamaguchi lists explainable AI (XAI), interpretability, machine learning, and time series data mining as research topics. In the papers listed in the researcher profile, the work deals with AI that classifies time series data, and with methods that let people interpret trained classifiers.
View More ↗MembersFaculty
- ProfessorAkihiro Yamaguchi
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Meteorological station 2” Jetam2 / CC BY-SA 4.0 (cropped)
- Related lab (Machine Learning and Explainable AI Lab): “Unsupervised-Learning” Rakshath / CC BY-SA 4.0 (cropped)
- Related lab (Numerical Analysis and Computational Fluid Dynamics Lab): “Biomechanics-Femur-Fracture-Nail-Internal Fixation-CAD-FEM” Fry72 Karel Frydrýšek, Tomáš Halo, Daniel Čepica / CC BY-SA 4.0 (cropped)
- Related lab (Numerical Analysis and Computational Fluid Dynamics Lab): “AbsoluteErrorNumericalDifferentiationExample” Berland / Public domain (cropped)
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