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SAMPLES: Self Adaptive Mining of Persistent Lexical
Snippets for Classifying Mobile Application Traffic
Honey Lao, Ryan Iranian, Alok Tongaonkar, Department of EEC,
University of Michigan, Ann Arbor,
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What is samples self adaptive mining?
Samples self adaptive mining is a process where algorithms adjust themselves to new data without human intervention.
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Companies or individuals that utilize self adaptive mining techniques are required to file samples self adaptive mining.
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Samples self adaptive mining can be filled out by providing information about the algorithms used, data sources, and any adjustments made.
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The purpose of samples self adaptive mining is to continuously improve the accuracy and efficiency of data analysis.
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Information such as algorithm updates, data sources, and performance metrics must be reported on samples self adaptive mining.
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