Description:

Elefant is an open source software platform for the Machine Learning community licensed under the Mozilla Public License (MPL) and developed using Python, C, and C++. We aim to make it the platform of choice for quick prototyping and deploying machine learning algorithms. Elefant includes modules for many common optimization problems arising in machine learning and inference. The key feature of Elefant is its light weight component based design. Its design allows reuse of various components within the Elefant framework and provides a mechanism to inter operate or easily integrate with external software systems. Its easy to use graphical user interface allows quick prototyping or testing of new machine learning algorithms and its scripting API interface allows advance application development.

Changes to previous version:

This release contains the Stream module as a first step in the direction of providing C++ library support. Stream aims to be a software framework for the implementation of large scale online learning algorithms. Large scale, in this context, should be understood as something that does not fit in the memory of a standard desktop computer.

Added Bundle Methods for Regularized Risk Minimization (BMRM) allowing to choose from a list of loss functions and solvers (linear and quadratic).

Added the following loss classes: BinaryClassificationLoss, HingeLoss, SquaredHingeLoss, ExponentialLoss, LogisticLoss, NoveltyLoss, LeastMeanSquareLoss, LeastAbsoluteDeviationLoss, QuantileRegressionLoss, EpsilonInsensitiveLoss, HuberRobustLoss, PoissonRegressionLoss, MultiClassLoss, WinnerTakesAllMultiClassLoss, ScaledSoftMarginMultiClassLoss, SoftmaxMultiClassLoss, MultivariateRegressionLoss

Graphical User Interface provides now extensive documentation for each component explaining state variables and port descriptions.

Changed saving and loading of experiments to XML (thereby avoiding storage of large input data structures).

Unified automatic input checking via new static typing extending Python properties.

Full support for recursive composition of larger components containing arbitrary statically typed state variables.

BibTeX Entry: Download

Supported Operating Systems: Linux, Macosx, Windows

Data Formats: Svmlight, Ascii, Matlab, Svmlight With Header

Tags: String Kernel, Support Vector Machine, Python, Regression, Kernels, Online Learning, Feature Selection, Classifiaction, Nips2008

Archive: download here

Other available revisons

Version	Changelog	Date
0.4	This release contains the Stream module as a first step in the direction of providing C++ library support. Stream aims to be a software framework for the implementation of large scale online learning algorithms. Large scale, in this context, should be understood as something that does not fit in the memory of a standard desktop computer. Added Bundle Methods for Regularized Risk Minimization (BMRM) allowing to choose from a list of loss functions and solvers (linear and quadratic). Added the following loss classes: BinaryClassificationLoss, HingeLoss, SquaredHingeLoss, ExponentialLoss, LogisticLoss, NoveltyLoss, LeastMeanSquareLoss, LeastAbsoluteDeviationLoss, QuantileRegressionLoss, EpsilonInsensitiveLoss, HuberRobustLoss, PoissonRegressionLoss, MultiClassLoss, WinnerTakesAllMultiClassLoss, ScaledSoftMarginMultiClassLoss, SoftmaxMultiClassLoss, MultivariateRegressionLoss Graphical User Interface provides now extensive documentation for each component explaining state variables and port descriptions. Changed saving and loading of experiments to XML (thereby avoiding storage of large input data structures). Unified automatic input checking via new static typing extending Python properties. Full support for recursive composition of larger components containing arbitrary statically typed state variables.	October 17, 2009, 08:48:19
0.3	Initial Announcement on mloss.org.	November 30, 2007, 10:24:19

Comments

Gorden Jemwa (on December 1, 2007, 16:34:06)

I've tried installing the software but running into problems; perhaps someone can help. Below is what I do and what I get. When I run the setup.py build command the installer complains of an unnamed module numpy but I already have the latest installed (see next output from setup-deps.py). >> $ /usr/bin/python2.5 setup-deps.py Reading package lists... Building dependency tree... python-wxgtk2.8 is already the newest version. 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. Reading package lists... Building dependency tree... python-matplotlib is already the newest version. 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. Reading package lists... Building dependency tree... python-numpy is already the newest version. 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. Reading package lists... Building dependency tree... python-scipy is already the newest version. 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. pythonPackageDir : ... -------------------------------------------------- Check installation : wx Check installation : matplotlib Check installation : numpy Check installation : scipy All modules successfully installed.

Gorden Jemwa (on December 1, 2007, 19:41:01)

Please ignore my earlier post. I'm referring it to the more appropriate elefant-users forum.

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