New answers tagged machine-learning
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Accepted
Learning with zero inductive bias
To be more precise, if you want a distribution-free generalization bound, then you must have some inductive bias (these are the no-free-lunch theorems referenced by D.W.). For binary classification, ...
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Learning with zero inductive bias
You cannot. See the No free lunch theorem (e.g., here and here and here and many other resources).
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Learning positive half-lines (in $\mathbb{N}$)
Yes, it's trivial. The learning algorithm consists of choosing the smallest positive example $x_0$, and taking the hypothesis to be $h(x)=1[x\ge x_0]$. All of the generalization guarantees proved for $...
- 10.3k
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Accepted
Learning arithmetic series
An arithmetic series is defined by the 1st term $t_1$ and the difference between terms $d$. If you stipulate that $\max(|t_1|,d)\le M$ then you have a finite hypothesis space and hence a finite ...
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Why is the estimation error smaller in Structural Risk Minimization
The answer is right under my noise but I'd failed to see it.
Theorem 7.4 in the book says that:
$$
\mathbb{P}\lbrace S\sim\mathcal{D}^m: (\forall h\in\mathcal{H}) (L_\mathcal{D}(h)\le L_S(h) + \...
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Unable to understand the Sample complexity of PAC learning
I don't understand exactly your question, but I'll answer it from the two possible misunderstandings I can see.
The first confusion comes from your definition of the function $m_\mathcal{H} : (0, 1)^2 ...
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