Lasso type classifiers with a reject option

05/16/2007
by   Marten Wegkamp, et al.
0

We consider the problem of binary classification where one can, for a particular cost, choose not to classify an observation. We present a simple proof for the oracle inequality for the excess risk of structural risk minimizers using a lasso type penalty.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/17/2015

Oracle inequalities for ranking and U-processes with Lasso penalty

We investigate properties of estimators obtained by minimization of U-pr...
research
08/13/2015

Neyman-Pearson Classification under High-Dimensional Settings

Most existing binary classification methods target on the optimization o...
research
02/16/2017

A new concentration inequality for the excess risk in least-squares regression with random design and heteroscedastic noise

We prove a new concentration inequality for the excess risk of a M-estim...
research
07/23/2021

A note on sharp oracle bounds for Slope and Lasso

In this paper, we study the sharp oracle bounds for Slope and Lasso and ...
research
12/25/2022

Simple proof of the risk bound for denoising by exponential weights for asymmetric noise distributions

In this note, we consider the problem of aggregation of estimators in or...
research
09/11/2019

Aggregated Hold-Out

Aggregated hold-out (Agghoo) is a method which averages learning rules s...
research
07/21/2020

The Complete Lasso Tradeoff Diagram

A fundamental problem in the high-dimensional regression is to understan...

Please sign up or login with your details

Forgot password? Click here to reset