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Afaik what you're talking about is more commonly referred to as "semi-supervised learning". Transduction is a more specific case of semi-supervised learning whe
by benanne 12y ago
Afaik what you're talking about is more commonly referred to as "semi-supervised learning". Transduction is a more specific case of semi-supervised learning where you know the test set, i.e. you know which data points the model will have to make predictions for. That means you can exploit this data for training the model, for example by unsupervised pre-training or with a pseudo-labeling approach.
- murbard2 12y agoBayesians don't need a test set 8)
- turnersr 12y agoWhy is that?
- murbard2 12y agoBecause the prior on your parameters smooths out the prediction. Most cookbook techniques such as ridge regressions, cross-validation, etc have a Bayesian interpretation as a prior on the parameter. Bayesian techniques allow you to use all the data available. That said, sometimes they are computationally expensive, and it's better to approximate them by using a test set.
- moultano 12y agoUnless you have an infinite regress on priors for your priors, and uncomputable Komolgorov penalties on the structure of your model, I think you need a test set. (This means you need a test set.)
- ced 12y agoThere's an interesting chapter in MacKay's book on Occam's razor. I'm not sure how I feel about it, but it's very thought-provoking.
- GFK_of_xmaspast 12y agoIf your priori are that strong, why bother with the data?
- Totient 12y agoYou don't need a validation set. I'm pretty sure you still want a test set.
- deleted 12y ago[deleted]
- murbard2 12y agoNuh uh, anyone doing LOCV is basically using AIC. There are also other principles, such as MDL which do not rely on a test set.