swMATH ID: 14411
Software Authors: Müller, Andreas C.; Behnke, Sven
Description: Pystruct-learning structured prediction in Python. Structured prediction methods have become a central tool for many machine learning applications. While more and more algorithms are developed, only very few implementations are available. {it pystruct} aims at providing a general purpose implementation of standard structured prediction methods, both for practitioners and as a baseline for researchers. It is written in Python and adapts paradigms and types from the scientific Python community for seamless integration with other projects.
Homepage: http://jmlr.csail.mit.edu/papers/volume15/mueller14a/mueller14a.pdf
Keywords: prediction; structural support vector machines; conditional random fields; python
Related Software: CRFsuite; SVMstruct; Python; CRF++; Dlib-ml; seqlearn; Penn Treebank; IllinoisSL; PyTorch; Dyna; TensorFlow; Pyro; Torch-Struct; DeepLab; U-Net; GitHub; StructED; LibDAI; Scikit; CVXOPT
Cited in: 3 Documents

Standard Articles

1 Publication describing the Software, including 1 Publication in zbMATH Year
Pystruct-learning structured prediction in Python. Zbl 1318.68150
Müller, Andreas C.; Behnke, Sven

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