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### 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).
1 vote

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 $... 1 vote Accepted ### Learning arithmetic series An arithmetic series is defined by the 1st term$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 ... 0 votes ### 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) + \... 0 votes Accepted ### 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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