Encog: Library of Interchangeable Machine Learning Models for Java and C#
This provides a practical tool for developers in Java and C# ecosystems to apply various machine learning models with minimal recoding, but it is incremental as it builds on existing library concepts.
The paper introduces the Encog library for Java and C#, a scalable and adaptable machine learning framework that supports interchangeable models for regression, classification, and clustering, with efficient multithreaded code to reduce training time on multicore processors.
This paper introduces the Encog library for Java and C#, a scalable, adaptable, multiplatform machine learning framework that was 1st released in 2008. Encog allows a variety of machine learning models to be applied to datasets using regression, classification, and clustering. Various supported machine learning models can be used interchangeably with minimal recoding. Encog uses efficient multithreaded code to reduce training time by exploiting modern multicore processors. The current version of Encog can be downloaded from http://www.encog.org.