Elefant (Efficient Learning, Large-scale Inference, and Optimisation Toolkit) is an open source library for machine learning

Elefant include modules for many common optimisation problems arising in machine learning and inference. It is designed to be modular and easy to use. Framework provides easy to use python interface, which can be use for quick prototyping and testing inference algorithms.

The diagram below illustrates the high-level system view of Elefant. For more information browse the documentation.

What does Elefant Do?

Following are the key features of Elefant:

• Light weight component based system design, plug and play kind of a architecture
• Component suite for basic as well as advanced machine learning algorithms
• Support for various data source formats
• Components for data visualizations
• Easy to use graphical user interface for visual programming and quick prototyping
• Intuitive application programming interface for advanced prototyping
• Python wrappers for high-performance parallel scientific packages like PETSc, TAO, and SLEPc
• Interface to external systems like UIMA using jpype
• Comprehensive system documentation
• Open source and licensed under the Mozilla Public License (MPL)

Following machine learning algorithms are implemented in the Elefant:

• Support Vector Machine (SVM) for classification, regression, quantile and novelty detection, online learning, Epsilon Insensitive and Laplacian support vector regression.
• Gaussian Process Regression, Heteroscedastic Gaussian Process regression, Multi-class transductive classification with Gaussian Process.
• Solvers for the quadratic programming problem
• BAHSIC feature selection
• Algorithms for fast computation and manipulation of kernel matrices. Linear, RBF, Dot Product and String Kernels
• Loopy Belief Propagation and Junction Tree algorithms.
• Cover Tree for calculating the nearest neighbor

For future plans browse the Roadmap.