233 research outputs found
No Free Lunch for Noise Prediction
No-free-lunch theorems have shown that learning algorithms cannot be universally good. We show that no free funch exists for noise prediction as well. We show that when the noise is additive and the prior over target functions is uniform, a prior on the noise distribution cannot be updated, in the Bayesian sense, from any finite data set. We emphasize the importance of a prior over the target function in order to justify superior performance for learning systems
Embedding a Forest in a Graph
For \math{p\ge 1}, we prove that every forest with \math{p} trees whose sizes
are can be embedded in any graph containing at least
vertices and having a minimum degree at least
.Comment: Working paper, submitte
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