Humans Learn Using Manifolds, Reluctantly
Abstract
When the distribution of unlabeled data in feature space lies along a manifold, the information it provides may be used by a learner to assist classification in a semi-supervised setting. While manifold learning is well-known in machine learning, the use of manifolds in human learning is largely unstudied. We perform a set of experiments which test a human's ability to use a manifold in a semi-supervised learning task, under varying conditions. We show that humans may be encouraged into using the manifold, overcoming the strong preference for a simple, axis-parallel linear boundary.
Cite
Text
Rogers et al. "Humans Learn Using Manifolds, Reluctantly." Neural Information Processing Systems, 2010.Markdown
[Rogers et al. "Humans Learn Using Manifolds, Reluctantly." Neural Information Processing Systems, 2010.](https://mlanthology.org/neurips/2010/rogers2010neurips-humans/)BibTeX
@inproceedings{rogers2010neurips-humans,
title = {{Humans Learn Using Manifolds, Reluctantly}},
author = {Rogers, Tim and Kalish, Chuck and Harrison, Joseph and Zhu, Xiaojin and Gibson, Bryan R.},
booktitle = {Neural Information Processing Systems},
year = {2010},
pages = {730-738},
url = {https://mlanthology.org/neurips/2010/rogers2010neurips-humans/}
}