Learning to Learn: Algorithmic Inspirations from Human Problem Solving
Abstract
We harness the ability of people to perceive and interact with visual patterns in order to enhance the performance of a machine learning method. We show how we can collect evidence about how people optimize the parameters of an ensemble classification system using a tool that provides a visualization of misclassification costs. Then, we use these observations about human attempts to minimize cost in order to extend the performance of a state-of-the-art ensemble classification system. The study highlights opportunities for learning from evidence collected about human problem solving to refine and extend automated learning and inference.
Cite
Text
Kapoor et al. "Learning to Learn: Algorithmic Inspirations from Human Problem Solving." AAAI Conference on Artificial Intelligence, 2012. doi:10.1609/AAAI.V26I1.8343Markdown
[Kapoor et al. "Learning to Learn: Algorithmic Inspirations from Human Problem Solving." AAAI Conference on Artificial Intelligence, 2012.](https://mlanthology.org/aaai/2012/kapoor2012aaai-learning/) doi:10.1609/AAAI.V26I1.8343BibTeX
@inproceedings{kapoor2012aaai-learning,
title = {{Learning to Learn: Algorithmic Inspirations from Human Problem Solving}},
author = {Kapoor, Ashish and Lee, Bongshin and Tan, Desney S. and Horvitz, Eric},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2012},
pages = {1571-1577},
doi = {10.1609/AAAI.V26I1.8343},
url = {https://mlanthology.org/aaai/2012/kapoor2012aaai-learning/}
}