Ruderman, Avraham Pinchas
Many machine learning models are based on similarities between
new examples and previously observed examples. Such models are
extremely flexible and can adapt to a wide range of tasks.
However, if examples are composed of many variables, then even if
we collect a large number of examples, it is possible that no two
examples will be significantly similar. This, in turn, means that
a learning algorithm may require an unreasonably large number of
examples to...[Show more]
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.