Published June 6, 2004
| Submitted
Technical Report
Open
The Bin Model
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.
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Submitted - binmodel.pdf
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Additional details
- Eprint ID
- 27073
- Resolver ID
- CaltechCSTR:2004.002
- Created
-
2004-07-06Created from EPrint's datestamp field
- Updated
-
2019-10-03Created from EPrint's last_modified field
- Caltech groups
- Computer Science Technical Reports
- Series Name
- Computer Science Technical Reports
- Series Volume or Issue Number
- 2004.001