[Shogun] A large scale machine learning toolbox

Please see: http://www.shogun-toolbox.org/page/features/

A comparison of shogun with the popular machine learning toolboxes weka, kernlab, dlib, nieme, orange, java-ml, pyML, mlpy, pybrain, torch3, scikit-learn. A '?' denotes unkown, '-' feature is missing. This table is availabe as a google spreadsheet. If you have additions please share an updated spreadsheet with us and we will integrate the changes.

	created	last updated	main language	main focus
shogun	1999	10-2013	C++	General Purpose ML Package with particular focus on large scale learning; Kernel Methods; Interfaces to various languages
weka	1997	07-2013	java	General Purpose ML Package
kernlab	04-2004	11-2013	R	Kernel Based Classification/Dimensionality Reduction
dlib	2006	10-2013	C++	Portability; Correctness
nieme	09-2006	03-2009	C++	Linear Regression; Ranking; Classification
orange	06-2004	11-2013	python	Visual Data Analysis
java-ml	08-2008	07-2012	java	Feature Selection
pyML	08-2004	09-2013	C++; python	Kernel Methods
mlpy	02-2008	03-2012	python	Basic Algorithms
pybrain	10-2008	02-2013	python	Reinforcement Learning
torch7	01-2002	11-2013	C++;lua	Neural Networks
scikit-learn	2007	08-2013	python; cython	General Purpose with simple API and numpy / scipy idioms

		shogun	weka	kernlab	dlib	nieme	orange	java-ml	pyML	mlpy	pybrain	torch3	scikit-learn
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
	C#
	Lua
	Ruby
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
	multiclass MKL
Classifiers	Naive Bayes
	Bayesian Networks
	Multi Layer Perceptron
	RBF Networks
	Logistic Regression
	LASSO
	Decision Trees
	k-NN
	Gaussian Process Classification
Linear Classifiers	Linear Programming Machine
	LDA
Distributions	Markov Chains
	Hidden Markov Models
Dimension Reduction	PCA
	Kernel PCA
	Isomap
	Multidimensional scaling
	Sammon mapping
	Locally Linear Embedding
	Diffusion Map
	Local Tangent Space Alignment
	Laplacian Eigenmaps
	Barnes-Hut t-SNE
Independent Component Analysis	FIXME
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
Structural Output Learning	Label Sequence Learning
	Factor Graph Learning
	SO-SGD
	Latent SO-SVM
Supported File Formats	Binary
	Arff
	HDF5
	CSV
	libSVM/ SVMLight format
	Excel
	Protobuf
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)