Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published March 2010 | Published
Journal Article Open

SFO: A Toolbox for Submodular Function Optimization

Abstract

In recent years, a fundamental problem structure has emerged as very useful in a variety of machine learning applications: Submodularity is an intuitive diminishing returns property, stating that adding an element to a smaller set helps more than adding it to a larger set. Similarly to convexity, submodularity allows one to efficiently find provably (near-) optimal solutions for large problems. We present SFO, a toolbox for use in MATLAB or Octave that implements algorithms for minimization and maximization of submodular functions. A tutorial script illustrates the application of submodularity to machine learning and AI problems such as feature selection, clustering, inference and optimized information gathering.

Additional Information

© 2010 Andreas Krause. Submitted 6/09; Published 3/10. Editor: Soeren Sonnenburg. This research was supported by ONR grant N00014-09-1-1044, NSF CNS-0932392, a gift from Microsoft Corporation and an Okawa Foundation Research Grant.

Attached Files

Published - Krause2010p10074J_Mach_Learn_Res.pdf

Files

Krause2010p10074J_Mach_Learn_Res.pdf
Files (39.4 kB)
Name Size Download all
md5:d80e0ffa9e347cee28f5ca354ec65af8
39.4 kB Preview Download

Additional details

Created:
August 19, 2023
Modified:
October 20, 2023