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The Bin Model

Abu-Mostafa, Yaser and Song, Xubo and Nicholson, Alexander and Magdon-Ismail, Malik (2004) The Bin Model. California Institute of Technology , Pasadena, CA. (Unpublished) http://resolver.caltech.edu/CaltechCSTR:2004.002

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Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechCSTR:2004.002

Abstract

We propose a novel theoretical framework for understanding learning and generalization which we will call the bin model. Using the bin model, a closed form is derived for the generalization error that estimates the out-of-sample performance in terms of the in-sample performance. We address the problem of overfitting, and show that using a simple exhaustive learning algorithm it does not arise. This is independent of the target function, input distribution and learning model, and remains true even with noisy data sets. We apply our analysis to both classification and regression problems and give an example of how it may be used effectively in practice.


Item Type:Report or Paper (Technical Report)
Group:Computer Science Technical Reports
Subject Keywords:Learning theory, generalization, noisy data, VC dimension, out of sample error, overfitting
Record Number:CaltechCSTR:2004.002
Persistent URL:http://resolver.caltech.edu/CaltechCSTR:2004.002
Usage Policy:You are granted permission for individual, educational, research and non-commercial reproduction, distribution, display and performance of this work in any format.
ID Code:27073
Collection:CaltechCSTR
Deposited By: Imported from CaltechCSTR
Deposited On:06 Jul 2004
Last Modified:26 Dec 2012 14:14

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