December
27, 2013 2PM 302-308
In this talk, I will explain contemporary
machine learning methods utilizing theoretical properties of nearest
neighbors. The theoretical study for nearest neighbor information goes back
to T. Cover and P. Hart’s work in the 1960s which is based on the asymptotic
behavior of nearest neighbor classification with many data. Their best-known
contribution is the upper bound of the error in the asymptotic situation,
which is twice the Bayes error, as well as the idea of connecting nearest
neighbor information to the underlying probability density functions. I will
provide several examples about how nearest neighbor information can be better
utilized in the theoretical context, as well as the explanation on the
related topics of supervised machine learning theories. The explanation is
mostly based on my recent work, but papers of leading researchers in this
field will be introduced as well, which are appeared in recent NIPS and
AISTATS conferences and in several statistics journals. |