Skip to main content

HPCC Systems blog contributors are engineers and data scientists who for years have enabled LexisNexis customers to use big data to fulfill critical missions, gain competitive advantage, or unearth new discoveries. Check this blog regularly for insights into how HPCC Systems technology can put big data to work for your own organization.

Roger Dev on 10/05/2018
Decision Tree based learning methods have proven to be some of the most accurate and easy-to-use Machine Learning mechanisms. We call these mechanisms "Learning Trees". We explore the hows and whys of the various Learning Tree methods and provide an overview of our recently upgraded LearningTrees bundle.
Roger Dev on 08/30/2018
Cause and effect lie at the heart of human discourse and knowledge. Yet computer science and mathematics has very little to say on the subject until recently. There are now algorithms that can detect patterns of cause and effect from data. We explore these mechanisms and how they relate to Machine Learning and Artificial Intelligence.
Roger Dev on 04/18/2018
The Myriad Interface allows users of the HPCC Systems Machine Learning bundles to execute multiple independent machine learning activities within a single interface invocation. Learn how this works and how to use it.
Roger Dev on 02/14/2018
HPCC Systems provides a rich set of Machine Learning tools. This article provides an overview of the available bundles, and a tutorial on how to install and use them.
Roger Dev on 01/22/2018
A quick but potent intro to Machine Learning for those who are new to the subject. This article provides enough of the basic theory and terminology to make you dangerous.
Roger Dev on 09/13/2017

One of the main pieces of preliminary work involved in the major refactoring of the HPCC Systems Machine Learning Library was to productize PBblas as the backbone for Matrix Operations. The Parallel-Block Basic Linear Algebra Subsystem (PBblas) provides a mechanism for adapting matrix operations to Big Data and parallel processing on HPCC Systems clusters.