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I would disagree, the assumption that the data has the same distribution in your training set as it will in application is not required at all, and in fact it i

I would disagree, the assumption that the data has the same distribution in your training set as it will in application is not required at all, and in fact it is often better to have more data around the edge of the classification set.

YeGoblynQueenne 8y ago
Like I say, the central assumption in PAC learning is that a data set is drawn from the same distribution as unseen data. Otherwise, there can be no guarantees that the model is capable of representing anything beyond its data set. That is not controversial. I'm surprised at your statement that distributional consistency is "not required at all" and I wonder where it's coming from. I don't understand what you mean by "data around the edge of the classification set".
- BrandoElFollito 8y ago
  Why should it be? You present an image and state that this is type A. Then another, with your B, etc. The images are independent, each holds a classification and how many you have from each type does not need to reflect their actual distribution.