Falsifiable implies Learnable
Machine Learning
2014-08-29 v1 Statistics Theory
Machine Learning
Statistics Theory
Abstract
The paper demonstrates that falsifiability is fundamental to learning. We prove the following theorem for statistical learning and sequential prediction: If a theory is falsifiable then it is learnable -- i.e. admits a strategy that predicts optimally. An analogous result is shown for universal induction.
Cite
@article{arxiv.1408.6618,
title = {Falsifiable implies Learnable},
author = {David Balduzzi},
journal= {arXiv preprint arXiv:1408.6618},
year = {2014}
}