In optimization or machine learning problems we are given a set of items,usually points in some metric space, and the goal is to minimize or maximize an objective function over some space of candidate solutions . Traditional algorithms cannot handle modern systems that require parallel real-time computations of infinite distributed streams from sensors such as GPS, audio or video that arrive to a cloud, or networks of weaker devices such as smartphones or robots . Core-set is a “small data” summarization of the input “big data”, where everypossible query has approximately the same answer on both data sets . The challenge is to design coresets with provable tradeoff between their size and approximation error . This survey summarizes such constructions in aretrospective way, that aims to unified and simplify the state-of-the-art. It aims to unify and simplify state- of the state of theart. The survey summarizes the constructions of such constructments in aretrying to simplify and unify the state. It also aims to

Author(s) : Dan Feldman

Links : PDF - Abstract

Code :

https://github.com/oktantod/RoboND-DeepLearning-Project




Keywords : state - simplify - data - survey - aims -

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