|| Statistical Learning of the Insensitive Parameter in Support Vector Models
||Kazuho Watanabe, Toyohashi University of Technology, Japan|
||D6-S1-T3: Classification II
||Monday, 19 July, 22:00 - 22:20
||Monday, 19 July, 22:20 - 22:40
We consider the estimation of the insensitive parameter $\varepsilon$ in statistical models with $\varepsilon$-insensitive loss functions. The properties of the maximum likelihood estimators are studied for the $\varepsilon$-insensitive hyperbolic secant model. Focusing on the $\varepsilon$-insensitive Laplace and Gauss models, we analyze the average generalization errors of maximum likelihood and Bayesian learning. It is shown that $\varepsilon$-insensitive models behave as regular statistical models if the true generating distribution is in the interior of the parameter space, whereas non-regularity arises at the endpoint of the parameter space.