Join GitHub today
GitHub is home to over 20 million developers working together to host and review code, manage projects, and build software together.
Fetching latest commit…
Cannot retrieve the latest commit at this time.
|Failed to load latest commit information.|
Cost-sensitive multiclass classification with Adaptive Regularization of Weights This is a simple and efficient implementation of the Adaptive Regularization of Weights (AROW) algorithm for classification (http://books.nips.cc/papers/files/nips22/NIPS2009_0611.pdf). It is in python and it relies on the very efficient sparse vector implementation by Liang Huang (http://web.engr.oregonstate.edu/~huanlian/software/hvector-1.0.tar.bz) which must be downloaded separately. While it is not as efficient as other implementations in C++ such as arowpp (http://code.google.com/p/arowpp/), it offers the following: multiclass classification cost-sensitive classification probability estimates In order to run it and see how the API is used, please download a version of the 20 newsgroups dataset available here: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#news20.binary and run (remember to install Liang Huang's sparse vector package first): python arow.py news20.binary For any questions or bugs please contact Andreas Vlachos (http://www.cl.cam.ac.uk/~av308/). If you find this software useful please acknowledge it using the link to this project. Thanks!