Data driven design for online industrial auctions
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Publication:2043444
DOI10.1007/s10472-020-09722-2OpenAlexW2953768291MaRDI QIDQ2043444
Qing Chuan Ye, Ying-Qian Zhang, Jason Rhuggenaath, Sicco Verwer, Michiel Jurgen Hilgeman
Publication date: 2 August 2021
Published in: Annals of Mathematics and Artificial Intelligence (Search for Journal in Brave)
Full work available at URL: https://research.tue.nl/nl/publications/67e2fb95-21c0-4357-95bb-6c0cb127d610
Mixed integer programming (90C11) Learning and adaptive systems in artificial intelligence (68T05) Auctions, bargaining, bidding and selling, and other market models (91B26)
Uses Software
Cites Work
- Multi-class AdaBoost
- Empirical decision model learning
- Auction optimization using regression trees and linear models as integer programs
- Learning decision trees with flexible constraints and objectives using integer optimization
- Optimization and analysis aid via data-mining for simulated production systems
- Optimal Auction Design
- Modeling and Forecasting Online Auction Prices: A Semiparametric Regression Analysis
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