Data driven design for online industrial auctions
type: Item
instance of: scholarly article
title: Data driven design for online industrial auctions
author: Qing Chuan Ye
author: Jason Rhuggenaath
author: Yingqian Zhang
author: Sicco Verwer
author: Michiel Jurgen Hilgeman
published in: Annals of Mathematics and Artificial Intelligence
publication date: 2021-08-02T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 90C11
Mathematics Subject Classification ID: 91B26
zbMATH DE Number: 7377244
zbMATH Keywords: machine learning
zbMATH Keywords: optimization
zbMATH Keywords: auction design
describes a project that uses: Gurobi
describes a project that uses: CPLEX
describes a project that uses: AdaBoost-SAMME
MaRDI profile type: Publication
OpenAlex ID: W2953768291
cites work: Modeling and Forecasting Online Auction Prices: A Semiparametric Regression Analysis
cites work: Multi-class AdaBoost
cites work: Optimization and analysis aid via data-mining for simulated production systems
cites work: Empirical decision model learning
cites work: Optimal Auction Design
cites work: Learning decision trees with flexible constraints and objectives using integer optimization
cites work: Auction optimization using regression trees and linear models as integer programs
DOI: 10.1007/S10472-020-09722-2
description: scientific article; zbMATH DE number 7377244