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|Title:||Fat-shattering and the learnability of real-valued functions|
|Citation:||Bartlett, P.L., Long, P.M., Williamson, R.C. (1996-06). Fat-shattering and the learnability of real-valued functions. Journal of Computer and System Sciences 52 (3) : 434-452. ScholarBank@NUS Repository. https://doi.org/10.1006/jcss.1996.0033|
|Abstract:||We consider the problem of learning real-valued functions from random examples when the function values are corrupted with noise. With mild conditions on independent observation noise, we provide characterizations of the learnability of a real-valued function class in terms of a generalization of the Vapnik-Chervonenkis dimension, the fat-shattering function, introduced by Kearns and Schapire. We show that, given some restrictions on the noise, a function class is learnable in our model if an only if its fat-shattering function is finite. With different (also quite mild) restrictions, satisfied for example by guassion noise, we show that a function class is learnable from polynomially many examples if and only if its fat-shattering function grows polynomially. We prove analogous results in an agnostic setting, where there is no assumption of an underlying function class. © 1996 Academic Press, Inc.|
|Source Title:||Journal of Computer and System Sciences|
|Appears in Collections:||Staff Publications|
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