The fundamental result of Li, Long, and Srinivasan on approximations of set systems has become a key tool across several communities such as learning theory, algorithms,  combinatorics and data analysis (described as `the pinnacle of a long sequence of papers') . The goal of this paper is to give a simpler , self-contained, modular proof of this result for finite set systems. The only ingredient we assume is the standard Chernoff's concentration bound. This makes the proof accessible to a wider audience, readers not familiar with techniques from statistical learning theory, and makes it possible to be covered in a single self-contained lecture in an algorithms course.
The fundamental result of Li, Long, and Srinivasan on approximations of set systems has become a key tool across several communities such as learning theory, algorithms, computational geometry, combinatorics and data analysis . The goal of this paper is to give a modular , self-contained, intuitive proof of this result for finite set systems. The only ingredient we assume is the standard Chernoff's concentration bound. This makes the proof accessible to a wider audience, readers not familiar with techniques from statistical learning theory, and makes it possible to be covered in a single self-contained lecture in a geometry, algorithms or combinatorics course.
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computational geometry,
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(described as `the pinnacle of a long sequence of papers')
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simpler
modular
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modular
intuitive
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an algorithms
a geometry, algorithms or combinatorics