Author:
ANTHONY MARTIN,BARTLETT PETER L.
Abstract
In this paper, we study a statistical property of classes of real-valued functions that we call
approximation from interpolated examples. We derive a characterization of function classes
that have this property, in terms of their ‘fat-shattering function’, a notion that has proved
useful in computational learning theory. The property is central to a problem of learning
real-valued functions from random examples in which we require satisfactory performance
from every algorithm that returns a function which approximately interpolates the training
examples.
Publisher
Cambridge University Press (CUP)
Subject
Applied Mathematics,Computational Theory and Mathematics,Statistics and Probability,Theoretical Computer Science
Cited by
16 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献