Nieme: Large-Scale Energy-Based Models

Abstract

In this paper we introduce N IEME , a machine learning library for large-scale classification, regression and ranking. N IEME , relies on the framework of energy-based models (LeCun et al., 2006) 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. N IEME , can hence be used in a wide range of situations, but is particularly interesting for 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 original feature of the N IEME , toolbox. N IEME , is released under the GPL license. It is efficiently implemented in C++, it works on Linux, Mac OS X and Windows and provides interfaces for C++, Java and Python.