Team Composition in PES2018 Using Submodular Function Optimization
نویسندگان
چکیده
منابع مشابه
SFO: A Toolbox for Submodular Function Optimization
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....
متن کاملOn Unconstrained Quasi-Submodular Function Optimization
With the extensive application of submodularity, its generalizations are constantly being proposed. However, most of them are tailored for special problems. In this paper, we focus on quasi-submodularity, a universal generalization, which satisfies weaker properties than submodularity but still enjoys favorable performance in optimization. Similar to the diminishing return property of submodula...
متن کاملFast Semidifferential-based Submodular Function Optimization
We present a practical and powerful new framework for both unconstrained and constrained submodular function optimization based on discrete semidifferentials (suband super-differentials). The resulting algorithms, which repeatedly compute and then efficiently optimize submodular semigradients, offer new and generalize many old methods for submodular optimization. Our approach, moreover, takes s...
متن کاملMining Interesting Itemsets using Submodular Optimization
We propose a novel technique to retrieve itemsets that best explain a transaction database by leveraging a simple probabilistic model. Our approach is the first to infer such interesting itemsets directly from the transaction database using submodular function optimization and in so doing avoids many of the pitfalls commonly present in frequent itemset mining algorithms. Our proposed approach i...
متن کاملSubmodular Optimization with Submodular Cover and Submodular Knapsack Constraints
We investigate two new optimization problems — minimizing a submodular function subject to a submodular lower bound constraint (submodular cover) and maximizing a submodular function subject to a submodular upper bound constraint (submodular knapsack). We are motivated by a number of real-world applications in machine learning including sensor placement and data subset selection, which require ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2919447