  Expectile regression is a nice tool for investigating conditional
distributions beyond the conditional mean. It is well-known that expectiles can
be described with the help of the asymmetric least square loss function, and
this link makes it possible to estimate expectiles in a non-parametric
framework by a support vector machine like approach. In this work we develop an
efficient sequential-minimal-optimization-based solver for the underlying
optimization problem. The behavior of the solver is investigated by conducting
various experiments and the results are compared with the recent R-package
ER-Boost.
