Machine learning classifies data for enhanced algorithm performance in various applications. Lazy learners and eager learners represent key categories of classification algorithms. Effective data ...

But by using classification (linear discriminant analysis) and linear regression with Python, CSE has been able to isolate a variety of measurements and historical data they can use to train machine ...

Opinion: Lidiya Mishchenko and Pooya Shoghi explain how to bridge a gap preventing successful patent claims to protect new ...

Classification: In supervised learning, ... Many of the aforementioned machine learning applications, including facial recognition and ML-based image upscaling, ...

Artificial intelligence (AI) and machine learning (ML) are transforming our world. When it comes to these concepts there are important differences between supervised and unsupervised learning.

Tailoring Therapy for Children With Neuroblastoma on the Basis of Risk Group Classification: Past, Present, and Future Machine learning (ML) has the potential to transform oncology and, more broadly, ...

TensorFlow 2.0, released in October 2019, revamped the framework significantly based on user feedback. The result is a machine learning framework that is easier to work with—for example, by ...

More information: Yun-Fei Shi et al, Machine Learning for Chemistry: Basics and Applications, Engineering (2023). DOI: 10.1016/j.eng.2023.04.013 Provided by Engineering ...