%0 Journal Article %1 balduzzi2014falsifiable %A Balduzzi, David %D 2014 %K bounds generalization learning theory %T Falsifiable implies Learnable %U http://arxiv.org/abs/1408.6618 %X 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.

@article{balduzzi2014falsifiable, 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.}, added-at = {2019-08-12T18:55:24.000+0200}, author = {Balduzzi, David}, biburl = {https://www.bibsonomy.org/bibtex/20b9a1b51ee3e3c95c1ead8bec7223974/kirk86}, description = {[1408.6618] Falsifiable implies Learnable}, interhash = {dfcfe2849c808f7c58d1042e7efff8df}, intrahash = {0b9a1b51ee3e3c95c1ead8bec7223974}, keywords = {bounds generalization learning theory}, note = {cite arxiv:1408.6618}, timestamp = {2019-08-12T18:55:24.000+0200}, title = {Falsifiable implies Learnable}, url = {http://arxiv.org/abs/1408.6618}, year = 2014 }