Predictive Analytics: An Optimization Perspective

被引：0

作者：

Rodrigues, Luis ^{[1
]}

Givigi, Sidney N. ^{[2
]}

机构：

[1] Concordia Univ, Dept Elect & Comp Engn, Montreal, PQ H3G 1M8, Canada

[2] Queens Univ, Sch Comp, Kingston, ON K7K7B4, Canada

来源：

IEEE ACCESS | 2024年 / 12卷

关键词：

Optimization; Predictive analytics; Vectors; Convex functions; Vehicle dynamics; Support vector machines; Predictive models; Classification; neural networks; predictive analytics; regression; DESIGN;

D O I：

10.1109/ACCESS.2024.3434617

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Predictive analytics is concerned with making predictions of future outcomes based on past data using data statistics, machine learning, dynamic models and filtering algorithms. This paper provides a review of the theory of predictive analytics from an optimization perspective, including some new developments in convex optimization and quadratic neural networks. Several examples from many different areas show how the theory can be applied to analyze data and provide useful information to decision makers.

引用

页码：106983 / 106995

页数：13

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