The Common-directions Method for Regularized Empirical Risk Minimization

For this class of losses, we provide a bias-variance decomposition and show that the assumptions commonly made in least-squares regression, such as the source and capacity

Leaves broadly ovate, oblong to elliptic, sessile to short-petiolate, 8–43 cm; blades 7–35 × 4.5–16.0 cm, entire, slightly revolute, base aequilateral to slightly oblique or

• both non-probabilistic approaches (i.e., the hybrid MC-FIA and the MC-based DS-IRS methods) always lead to more conservative results than the probabilistic approaches

We study the performance of empirical risk minimization (ERM), with re- spect to the quadratic risk, in the context of convex aggregation, in which one wants to construct a

The problem of convex aggregation is to construct a procedure having a risk as close as possible to the minimal risk over conv(F).. Consider the bounded regression model with respect

We have shown in Section 2 that the small-ball condition is satis…ed for linear models such as histograms, piecewise polynomials or compactly supported wavelets, but with constants

We prove that, if all the M classifiers are binary, the (penalized) Empirical Risk Minimization procedures are suboptimal (even under the margin/low noise condition) when the

We observe two factors of coupling (i. the rise of the average distance of transport and the increasing market share of road transport) and two factors of decoupling (the