Supplementary Material to
The Shogun Machine Learning Toolbox - JMLR Publication

Soeren Sonnenburg, Gunnar Raetsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien,
Fabio de Bona, Alexander Binder, Christian Gehl, and Vojtech Franc.
The SHOGUN Machine Learning Toolbox. Journal of Machine Learning Research, 11:1799-1802, June 2010.

Related Projects

	shogun	weka	kernlab	dlib	nieme	orange	java-ml	pyML	mlpy	pybrain	torch3
created	1999	1997	04-2004	2006	09-2006	06-2004	08-2008	08-2004	02-2008	10-2008	01-2002
last updated	03-2010	01-2010	10-2009	03-2010	03-2009	03-2010	08-2009	01-2009	11-2009	11-2009	11-2004
Main Language	C++	java	R	C++	C++	python	java	C++; python	python	python	C++
Main Focus	Large Scale Kernel Methods; String Features; SVMs	General Purpose ML Package	Kernel Based Classification/Dimensionality Reduction	Portability; Correctness	Linear Regression; Ranking; Classification	Visual Data Analysis	Feature Selection	Kernel Methods	Basic Algorithms	Reinforcement Learning	Kernel-based Classification

The pdf document with the machine learning toolbox feature comparison that we originally submitted to JMLR can be found here. An up-to-date version of this matrix is located at Google Spreadsheet. Please notify us about possible corrections and changes.

A comparison of shogun with the popular machine learning toolboxes weka, kernlab, dlib, nieme, orange, java-ml, pyML, mlpy, pybrain, torch3. A '?' denotes unkown, '-' feature is missing. This table is availabe as a google spreadsheet.

	feature	shogun	weka	kernlab	dlib	nieme	orange	java-ml	pyML	mlpy	pybrain	torch3
General Features	Graphical User Interface
	One Class Classification
	Classification
	Multiclass classification
	Regression
	Structured Output Learning
	Pre-Processing
	Built-in Model Selection Strategies
	Visualization
	Test Framework
	Large Scale Learning
	Semi-supervised Learning
	Multitask Learning
	Domain Adaptation
	Serialization
	Parallelized Code
	Performance Measures (auROC etc)
	Image Processing

Supported Operating Systems	Linux
	Windows
	Mac OSX
	Other Unix

Language Bindings	Python
	R
	Matlab
	Octave
	C/C++
	Command Line
	Java

SVM Solvers	SVMLight
	LibSVM
	SVM Ocas
	LibLinear
	BMRM
	LaRank
	SVMPegasos
	SVM SGD
	other

Regression	Kernel Ridge Regression
	Support Vector Regression
	Gaussian Processes
	Relevance Vector Machine

Multiple Kernel Learning	MKL
	q-norm MKL

Classifiers	Naive Bayes
	Bayesian Networks
	Multi Layer Perceptron
	RBF Networks
	Logistic Regression
	LASSO
	Decision Trees
	k-NN

Linear Classifiers	Linear Programming Machine
	LDA

Distributions	Markov Chains
	Hidden Markov Models

Kernels	Linear
	Gaussian
	Polynomial
	String Kernels
	Sigmoid Kernel
	Kernel Normalizer

Feature Selection	Forward
	Wrapper methods
	Recursive Feature Selection

Missing Features	Mean value imputation
	EM-based/model based imputation

Clustering	Hierarchical Clustering
	k-means

Optimization	BFGS
	conjugate gradient
	gradient descent
	bindings to CPLEX
	bindings to Mosek
	bindings to other solver

Supported File Formats	Binary
	Arff
	HDF5
	CSV
	libSVM/ SVMLight format
	Excel

Supported Data Types	Sparse Data Representation
	Dense Matrices
	Strings
	Support for native (e.g. C) types (char, signed and unsigned int8, int16, int32, int64, float, double, long double)

Class Design

Supplementary Material to The Shogun Machine Learning Toolbox - JMLR Publication

Related Projects

Feature matrix

Class Design

Supplementary Material to
The Shogun Machine Learning Toolbox - JMLR Publication