Nieme is a machine learning library for large-scale classification, regression and ranking. It relies on the framework of energy-based models which unifies several learning algorithms ranging from simple perceptrons to recent models such as the Pegasos support vector machine or L1-regularized maximum entropy models. This framework also unifies batch and stochastic learning which are both seen as energy minimization problems. Nieme can hence be used in a wide range of situations, but is particularly interesting for very-large-scale learning tasks where both the examples and the features are processed incrementally. Being able to deal with new incoming features at any time within the learning process is another key feature of the Nieme toolbox. Nieme is released under the GPL license. It is efficiently implemented in C++ and works on Linux, MacOS and Windows. Interfaces are available for C++, Java and Python.
- Changes to previous version:
Released Nieme 1.0
- BibTeX Entry: Download
- URL: Project Homepage
- JMLR MLOSS PaperURL: JMLR-MLOSS Paper Homepage
- Supported Operating Systems: Macosx, Windows, Unix
- Data Formats: None
- Tags: Structured Outputs, Classification, Regression, Online Learning, Gradient Based Learning, Large Scale Learning, Reinforcement Learning, Ranking, Nips2008
- Archive: download here
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