<commit_before>PyStruct - Structured Learning in Python
========================================
PyStruct aims at being an easy-to-use structured learning and prediction library.
Currently it implements only max-margin methods and a perceptron, but other algorithms
might follow.

The goal of PyStruct is to provide a well-documented tool for researchers as well as non-experts
to make use of structured prediction algorithms.
The design tries to stay as close as possible to the interface and conventions
of `scikit-learn <http://scikit-learn.org/dev>`_.<commit_msg>DOC say more names at the beginning of the intro.<commit_after>PyStruct - Structured Learning in Python
========================================
PyStruct aims at being an easy-to-use structured learning and prediction library.
Currently it implements only max-margin methods and a perceptron, but other algorithms
might follow. The learning algorithms implemented in PyStruct have various names,
which are often used loosely or differently in different communities.
Common names are conditional random fields (CRFs), maximum-margin Markov
random fields (M3N) or structural support vector machines.

The goal of PyStruct is to provide a well-documented tool for researchers as well as non-experts
to make use of structured prediction algorithms.
The design tries to stay as close as possible to the interface and conventions
of `scikit-learn <http://scikit-learn.org/dev>`_.
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