Nonlinear regression model generation using hyperparameter optimization-Reference-Cited by-同舟云学术

Nonlinear regression model generation using hyperparameter optimization

Published:2010-08 Issue:4 Volume:60 Page:981-988
ISSN:0898-1221
Container-title:Computers & Mathematics with Applications
language:en
Short-container-title:Computers & Mathematics with Applications

Author:

Strijov Vadim,Weber Gerhard Wilhelm

Publisher

Elsevier BV

Subject

Computational Mathematics,Computational Theory and Mathematics,Modeling and Simulation

Reference23 articles.

1. Inductive Learning Algorithms for Complex Systems Modeling;Madala,1994

2. Sel-Organising Data Mining: An Intelligent Approach to Extract Knowledge from Data;Mueler,1999

3. Genetic Programming IV: Routine Human-Competitive Machine Intelligence;Koza,2005

4. Analytic programming— symbolic regression by means of arbitrary evolutionary algorithms;Zelinka;Journal of Simulation,2004

5. E. Vladislavleva, Model-based problem solving through symbolic regression via Pareto genetic programming, Tilburg University, Tilburg, the Netherlands, 2008.

Cited by 23 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A mid-range approximation method assisted by trust region strategy for aerodynamic shape optimization;Applied Mathematical Modelling;2024-08

2. Proactive product warranty service planning and control: unravelling the boons of customer-generated content and multi-frequency analyses;Production Planning & Control;2024-06-10

3. An intelligent decision support system for warranty claims forecasting: Merits of social media and quality function deployment;Technological Forecasting and Social Change;2024-04

4. Multivariate time series forecasting of daily urban water demand using reinforcement learning and gated recurrent unit network;Proceedings of the 2024 7th International Conference on Data Storage and Data Engineering;2024-02-27

5. Fragility Index: A New Approach for Binary Classification;Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2023-08-04