Itay Evron, M.Sc. Thesis Seminar
Wednesday, 9.5.2018, 13:30
In extreme classification problems, machine learning algorithms are required
to map instances to labels from an extremely large label set.
We build on a recent extreme classification framework with logarithmic time and space,
and on a general approach for error correcting output coding (ECOC),
and introduce a flexible and efficient approach accompanied by bounds.
Our framework employs output codes induced by graphs,
and offers a tradeoff between accuracy and model size.
We show how to find the sweet spot of this tradeoff using
only the training data. Our experimental study demonstrates the
validity of our assumptions and claims, and shows the superiority
of our method compared with state-of-the-art algorithms.