On Overfitting Avoidance as Bias
In supervising learning it is commonly believed that penalizing complex functions help one avoid ``overfitting'' functions to data, and therefore improves generalization. It is also commonly believed that cross-validation is an effective way to choose amongst algorithms for fitting functions to data. In a recent paper, Schaffer (1993) presents experimental evidence disputing these claims. The current paper consists of a formal analysis of these contentions of Schaffer's. It proves that his contentions are valid, although some of his experiments must be interpreted with caution.
Year of publication: |
1993-03
|
---|---|
Authors: | Wolpert, David H. |
Institutions: | Santa Fe Institute |
Saved in:
Saved in favorites
Similar items by person
-
Estimating Functions of Probability Distributions From A Finite Set of Samples
Wolpert, David H., (1993)
-
Self-Dissimilarity: An Empirical Measure of Complexity
Wolpert, David H., (1997)
-
What Bayes Has to Say About the Evidence Procedure
Wolpert, David H., (1995)
- More ...