AI Chat AI Image Generator AI Video Text to Speech

On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel Based Methods

01/29/2013

∙

by Andreas Christmann, et al.

∙

∙

It is shown that bootstrap approximations of support vector machines (SVMs) based on a general convex and smooth loss function and on a general kernel are consistent. This result is useful to approximate the unknown finite sample distribution of SVMs by the bootstrap approach.

Andreas Christmann
12 publications
Robert Hable
6 publications

page 1

page 2

page 3

page 4

research

∙ 08/16/2023

On Neural Quantum Support Vector Machines

In <cit.> we introduced four algorithms for the training of neural suppo...

0 Lars Simon, et al. ∙

research

∙ 11/08/2011

On the stability of bootstrap estimators

It is shown that bootstrap approximations of an estimator which is based...

0 Andreas Christmann, et al. ∙

research

∙ 07/23/2010

Support Vector Machines for Additive Models: Consistency and Robustness

Support vector machines (SVMs) are special kernel based methods and belo...

0 Andreas Christmann, et al. ∙

research

∙ 05/25/2018

Function Estimation via Reconstruction

This paper introduces an interpolation-based method, called the reconstr...

0 Shifeng Xiong, et al. ∙

research

∙ 10/31/2015

Support Vector Regression, Smooth Splines, and Time Series Prediction

Prediction of dynamical time series with additive noise using support ve...

0 Raymundo Navarrete, et al. ∙

research

∙ 04/18/2015

On the consistency of Multithreshold Entropy Linear Classifier

Multithreshold Entropy Linear Classifier (MELC) is a recent classifier i...

0 Wojciech Marian Czarnecki, et al. ∙

research

∙ 05/30/2022

Deep Bootstrap for Bayesian Inference

For a Bayesian, the task to define the likelihood can be as perplexing a...

0 Lizhen Nie, et al. ∙