Most of the research in machine learning has been directed to the
problem of binary classification in which the learned classifier
outputs one of two possible answers. This is a fundamental problem,
but still it does not fit well important real-world applications. In
this tutorial we will focus on more complex settings in which there
are many possible answers with complex preference relationships among
them. Notable examples include multi-class categorization,
hierarchical classification, and sequence prediction.
We will use the algorithmic framework of online learning for several
reasons. First, in general online algorithms are conceptually simple
and easy to implement. Furthermore, online algorithms process one
example at a time. Thus, such methods are appealing for large data
sets. Second, online algorithms have been used in practice for the
applications that we will use as examples. Third, the analysis of
these algorithms is based on mathematical tools which are simpler than
those needed for analyzing other types of algorithms.
The goals of the tutorial :
- To provide the audience systematic tools to design and analyze
learning algorithms for their specific complex problems: from
binary classification through multi-class categorization, to
sequence prediction.
- To introduce new online algorithms which provide
state-of-the-art performance in practice, and are accompanied with
theoretical guarantees.
Schedule and Dates
- June 24th, 2007:
ICML Tutorial date.
Links
