In data analysis, it’s the outlier information that is usually the most interesting, yet sometimes that information goes unrecognized by the most common evaluation methods because they make inaccurate assumptions.
But now Michael Houle, a senior university lecturer at New Jersey Institute of Technology’s Ying Wu College of Computing, along with collaborators in Australia, Denmark and Serbia have become outliers themselves for developing the math to prove that breaking those assumptions can work better than conventional methods.
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