At the core of Rulex is a family of proprietary machine learning algorithms which are different from conventional machine learning algorithms, like Decision Trees, Neural Nets, and others.
Decision Trees and Random Forests produce predictive models that are readable, but not understandable.
Rulex allows overlapping rules for simpler, more understandable predictions than Decision Trees.
Neural Nets, as well as Support Vector Machines and most other popular math-based algorithms produce math functions that are neither readable nor understandable.
Unlike the binary trees and solving functions of conventional algorithms, Rulex works the way the human brain works, using learned logic to make decisions.
For migrating to Rulex and supporting legacy machine learning workloads, Rulex also supports most conventional machine learning techniques with a suite of popular algorithms with proprietary enhancements for higher performance and greater ease of use.
Machine learning for AI has never been easier, more economical, or more effective than it is with Rulex. Conventional methods are iterative, complex, and not transparent. Rulex is automatic, simple, and clear.
Neural Networks, SVMs, etc.
Users are data scientists
Requires math and programming
Manual data exploration
Manual, experimental modeling
Math function-based models
Black box predictive decisions
Prediction on central servers
New predictions require recoding
Rulex Logic Learning
Users are business/process experts
No new skills required
Automatic data discovery
Automatic model building
If-then rules-based models
Prediction on edge devices
Update predictions w/o recoding