The Lack of A Priori Distinctions Between Learning Algorithms¶
Wolpert, D. H. (1996). The Lack of A Priori Distinctions Between Learning Algorithms. Neural Computation, 8(7), 1341-1390.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Feature Engineering
- The no-free-lunch result says that without prior structure no learner outperforms any other on average; engineered features are how prior structure enters.
Supported in partVerified against the publisher's abstract
The abstract states the no-free-lunch symmetry — for any two algorithms there are as many targets favouring one as the other — but says nothing about engineered features as the vehicle of prior structure.
“it is shown, loosely speaking, that for any two algorithms A and B, there are “as many” targets (or priors over targets) for which A has lower expected OTS error than B as vice versa, for loss functions like zero-one loss.”
- The no-free-lunch result says that without prior structure no learner outperforms any other on average; engineered features are how prior structure enters.
- Inductive Reasoning
- The no-free-lunch theorem
This sourceWolpert no-free-lunch theorem machine learning generalization.
- The no-free-lunch theorem
Verification¶
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