Interactive Machine Learning
GPTKB entity
Statements (49)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
Machine Learning Paradigm
|
| gptkbp:appliesTo |
gptkb:Unsupervised_Learning
gptkb:Reinforcement_Learning Supervised Learning Semi-supervised Learning |
| gptkbp:benefit |
Faster Model Adaptation
Improved User Trust Reduced Data Requirements |
| gptkbp:challenge |
Scalability
Interface Design Bias Introduction User Fatigue |
| gptkbp:characteristic |
User Interaction
Human-in-the-loop Iterative Feedback |
| gptkbp:conference |
gptkb:CHI
gptkb:AAAI gptkb:ICML gptkb:NeurIPS |
| gptkbp:distinctFrom |
Traditional Machine Learning
|
| gptkbp:enables |
Personalization
Active Learning Model Refinement |
| gptkbp:fieldOfStudy |
gptkb:Machine_Learning
gptkb:artificial_intelligence |
| gptkbp:firstDescribed |
Early 2000s
|
| gptkbp:goal |
Improve Model Performance with User Feedback
Increase Usability of ML Systems Reduce Annotation Cost |
| https://www.w3.org/2000/01/rdf-schema#label |
Interactive Machine Learning
|
| gptkbp:notableContributor |
gptkb:James_Fogarty
Daniel S. Weld Saleema Amershi |
| gptkbp:notablePublication |
Amershi et al., 2014, 'Power to the People: The Role of Humans in Interactive Machine Learning'
Ware et al., 2001, 'Interactive Machine Learning for Data Visualization' Fails and Olsen, 2003, 'Interactive Machine Learning' |
| gptkbp:relatedTo |
gptkb:Human-Computer_Interaction
gptkb:Reinforcement_Learning Active Learning |
| gptkbp:requires |
User Feedback
Iterative Model Updates |
| gptkbp:usedIn |
gptkb:Computer_Vision
gptkb:Natural_Language_Processing Recommendation Systems Data Labeling Interactive Data Analysis Personalized Systems |
| gptkbp:bfsParent |
gptkb:IML
|
| gptkbp:bfsLayer |
7
|