# Tag Info

### Are PAC learning and VC dimension relevant to machine learning in practice?

Yes, PAC learning can be relevant in practice. There's an area of research that combines PAC learning and Bayesian learning that is called PAC-Bayesian (or PAC-Bayes) learning, where the goal is to ...
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### How does size of the dataset depend on VC dimension of the hypothesis class?

Given a hypothesis set $H$, the set of all possible mappings from $X\to Y$ where $X$ is our input space and $Y$ are our binary mappings: $\{-1,1\}$, the growth function, $\Pi_H(m)$, is defined as the ...
Accepted

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Accepted

### Is the VC Dimension meaningful in the context of Reinforcement Learning?

Yes, it is. This article (Approximate Planning in Large POMDPs via Reusable Trajectories) explain about it by means of the trajectory tree: A trajectory tree is a binary tree in which each node is ...
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1 vote
Accepted

### What do we mean by saying "VC dimension gives a LOOSE, not TIGHT bound"?

But, the literature (i.e. Learning from Data) states that VC gives a loose bound and that in real applications, learning models with lower VC dimension tend to generalize better than those with higher ...
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1 vote

### If a neural network is a universal function approximator, can it have any prior beliefs?

I think your deduction is mostly correct. Neural networks of depth are universal function approximators. This means that in principal, for any function of the form you describe, there's a NN that ...
• 8,867
1 vote

### Can feature engineering change the selection of the model according to the minimum description length?

I think the wrong assumption here is that you've forgotten the cost of encoding the new features! MDL should be considered relative to the original or raw dataset. The idea is that you want to find ...
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