There are many other problem settings. Here are a few.
In semi-supervised learning, we have a supervised-learning training set, but there may
be an additional set of x
values with no known y
. These values can still be used to
improve learning performance if they are drawn from Pr(X) that is the marginal of Pr(X, Y)
that governs the rest of the data set.
In active learning, it is assumed to be expensive to acquire a label y
human to read an x-ray image), so the learning algorithm can sequentially ask for particular
possible while minimizing the cost of labeling.
In transfer learning (also called meta-learning), there are multiple tasks, with data drawn
from different, but related, distributions. The goal is for experience with previous tasks to
apply to learning a current task in a way that requires decreased experience with the new
The kinds of assumptions that we can make about the data source or the solution include:
• The data are independent and identically distributed.
• The data are generated by a Markov chain.
• The process generating the data might be adversarial.
• The “true” model that is generating the data can be perfectly described by one of
some particular set of hypotheses.
The effect of an assumption is often to reduce the “size” or “expressiveness” of the space of
possible hypotheses and therefore reduce the amount of data required to reliably identify
an appropriate hypothesis.
Last Updated: 08/04/21 21:06:54