• 강사 : 노영균 (한양대 컴퓨터소프트웨어학부)
• 일시 : 2019. 10. 10 (목) 17:00~19:00

In spite of the consistency property in theory of nearest neighbor methods, which relates the algorithm to the theoretical minimum error, the Bayes error, algorithm using nearest neighbors is not preferred by researchers because it is too simple and old-fashioned. However, due to its simplicity, the analysis in nearest neighbor methods is tractable and can produce non-asymptotic theories. Those have simply not yet experienced a big enough number of data to enjoy theoretical prediction, and the current algorithmic and system technologies are immature. In this talk, I will introduce some of my recent works implementing models that modify the geometry around the points of interest and perform the nearest neighbor methods with many data as if we were using effectively even more data than what is actually given.

I will show some derivations of the equations to take advantage of the entire information within finite but many data and achieve the inference and estimation results seemingly as if we had used infinite data. By doing this, we believe nearest neighbor methods can be considered a breakthrough showing asymptotic performance by the smart usage of extremely many data.