The portfolio optimization model has limited impact in practice because of estimation issues when applied to real data. To address this, we adapt two machine learning methods, regularization and cross ...

In addition, the book includes an introduction to artificial neural networks, convex optimization, multi-objective optimization and applications of optimization in machine learning. About the Purdue ...

The field of artificial intelligence is undergoing a significant transformation with the rise of hybrid AI systems that ...

Learn how the Adagrad optimization algorithm works and see how to implement it step by step in pure Python — perfect for ...

Machine learning holds promise for optimizing treatment strategies and potentially improving outcomes in respiratory failure ...

When you listen to quantum enthusiasts, you may feel tempted to treat QML as an emerging silver bullet, which it isn’t.

Haojie Zhu, Ziyou Song, Weichao Zhuang, Heath Hofmann, Shuo Feng, Multi-objective Optimization for Connected and Automated Vehicles Using Machine Learning and Model Predictive Control, SAE ...

University of Sheffield researchers developed a machine learning framework to improve coating properties in a roll-to-roll slot die coating process. The work is seen as an initial step toward broader ...

The BO-GBRT model accurately predicts compressive strength in self-compacting concrete with recycled aggregates, improving ...

The convergence of artificial intelligence and cloud mining is creating unprecedented opportunities for the next generation of cryptocurrency enthusiasts.