Learning noisy functions via interval models
type: Item
instance of: scholarly article
title: Learning noisy functions via interval models
zbMATH Open document ID: 1198.93232
author: Giuseppe Calafiore
published in: Systems \& Control Letters
publication date: 2010-10-18T00:00:00Z
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93E35
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 5801136
zbMATH Keywords: set-valued models
zbMATH Keywords: interval approximation
zbMATH Keywords: convex optimization
zbMATH Keywords: identification
zbMATH Keywords: statistical learning
MaRDI profile type: Publication
full work available at URL: j.sysconle.2010.05.003
OpenAlex ID: W2040324073
cites work: On covariance function tests used in system identification
cites work: Interval predictor models: identification and reliability
cites work: Set-valued analysis
cites work: Q3324260
cites work: Impulse differential inclusions: a viability approach to hybrid systems
cites work: Set membership identification of nonlinear systems
cites work: Set membership prediction of nonlinear time series
cites work: Guaranteed non-linear estimation using constraint propagation on sets
cites work: Q2713275
cites work: Guaranteed recursive non-linear state bounding using interval analysis
cites work: Q4935997
cites work: Uncertain convex programs: randomized solutions and confidence levels
cites work: The Scenario Approach to Robust Control Design
cites work: The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs
cites work: On geometric optimization with few violated constraints
cites work: A simple sampling lemma: Analysis and applications in geometric optimization
cites work: Random Convex Programs
cites work: A sampling-and-discarding approach to chance-constrained optimization: feasibility and Optimality
DOI: 10.1016/J.SYSCONLE.2010.05.003
description: scientific article; zbMATH DE number 5801136