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info.json
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{
"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.\nWe 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.",
"authors": [
"Andreas Krause"
],
"id": "krause10a",
"issue": 38,
"pages": [
1141,
1144
],
"title": "SFO: A Toolbox for Submodular Function Optimization",
"volume": "11",
"year": "2010",
"special_issue": "MLOSS",
"extra_links": [["code", "https://las.inf.ethz.ch/sfo/index.html"]]
}