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. 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.

[1]  Thomas Hofmann,et al.  Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..

[2]  Sebastian Nowozin,et al.  A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Mark W. Schmidt,et al.  A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method , 2012, ArXiv.

[4]  Thorsten Joachims,et al.  Training structural SVMs when exact inference is intractable , 2008, ICML '08.

[5]  Sebastian Nowozin,et al.  Structured Learning and Prediction in Computer Vision , 2011, Found. Trends Comput. Graph. Vis..

[6]  Davis E. King,et al.  Dlib-ml: A Machine Learning Toolkit , 2009, J. Mach. Learn. Res..

[7]  Joris M. Mooij,et al.  libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models , 2010, J. Mach. Learn. Res..

[8]  Andrew McCallum,et al.  SampleRank: Training Factor Graphs with Atomic Gradients , 2011, ICML.

[9]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[10]  Nathan Ratliff,et al.  Online) Subgradient Methods for Structured Prediction , 2007 .

[11]  Trevor Darrell,et al.  Latent-Dynamic Discriminative Models for Continuous Gesture Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Thorsten Joachims,et al.  Cutting-plane training of structural SVMs , 2009, Machine Learning.

[14]  Tommi S. Jaakkola,et al.  Learning Efficiently with Approximate Inference via Dual Losses , 2010, ICML.

[15]  Thorsten Joachims,et al.  Learning structural SVMs with latent variables , 2009, ICML '09.

[16]  Vladlen Koltun,et al.  Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.