Please use this identifier to cite or link to this item:
|Title:||On the sample complexity of learning functions with bounded variation|
|Authors:||Long, Philip M.|
|Citation:||Long, Philip M. (1998). On the sample complexity of learning functions with bounded variation. Proceedings of the Annual ACM Conference on Computational Learning Theory : 126-133. ScholarBank@NUS Repository.|
|Abstract:||We show that the class FBV of [0, 1]-valued functions with total variation at most 1 can be agnostically learned with respect to the absolute loss in polynomial time from O (1/ε2 log 1/δ) examples, matching a known lower bound to within a constant factor. We establish a bound of O (1/m) on the expected error of a polynomial-time algorithm for learning FBV in the prediction model, also matching a known lower bound to within a constant factor. Applying a known algorithm transformation to our prediction algorithm, we obtain a polynomial-time PAC learning algorithm for FBV with a sample complexity bound of O (1/ε log 1/δ); this also matches a known lower bound to within a constant factor.|
|Source Title:||Proceedings of the Annual ACM Conference on Computational Learning Theory|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Nov 9, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.