In the big data era, machine learning optimization algorithms usually need to be designed and implemented on widely-used distributed computing platforms, such as Apache Hadoop, Spark, and Flink.

A Machine Learning Auxiliary Approach for the Distributed Dense RFID Readers Arrangement Algorithm Abstract: This paper is an extended version of the work published. Radio-frequency identification ...

These algorithms have evolved from early theoretical constructs into practical solutions that underpin modern cloud infrastructures, the Internet of Things (IoT) and even machine learning deployments.

The newly-open sourced Distributed Machine Learning Toolkit features fast, parallelized, and easy-to-deploy machine learning algorithms Topics Spotlight: AI-ready data centers ...

Training a machine learning algorithm to accurately solve complex problems requires large amounts of data. Previous articles in this series discussed an exascale-capable machine learning algorithm and ...

Japanese heavyweight NTT has come up with a way to carry out coordinated machine learning on multiple edge servers. It is similar to a blockchain or an artificial neural network, in that it is ...

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

Like with movies, I don’t have one favorite machine learning (ML) algorithm, but a few favorites, each for its own reason. Here are some of my top few algorithms and models: Most elegant: The ...

ADELPHI, Md.-- Army researchers discovered a way to quickly get information to Soldiers in combat using new machine learning techniques. The algorithms will play a significant role in enhancing ...