PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bootstraping Complex Functions
Bruno Apolloni, Simone Bassis, Sabrina Gaito and Dario Malchiodi
Nonlinear Analysis 2006.

Abstract

We formulate a new family of bootstrap algorithms suitable for learning non-Boolean functions from data. Within the Algorithmic Inference framework, the key idea is to consider a population of functions that are compatible with the observed sample. We generate items of this population from standard random seeds and reverse seed probabilities on the items. In this way we may compute in principle, and effectively achieve on paradigmatic examples, direct estimates and confidence intervals for any kind of complex function underlying the observed data according to any hypothesis on the randomness affecting the sample.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3523
Deposited By:Dario Malchiodi
Deposited On:11 February 2008