Shogun网站上的关于主流机器学习工具包的比较：
http://www.shogun-toolbox.org/page/features/
| created | last updated | main language | main focus | |
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| 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 |
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shogun |
weka |
kernlab |
dlib |
nieme |
orange |
java-ml |
pyML |
mlpy |
pybrain |
torch3 |
scikit-learn | ||
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| General Features | Graphical User Interface |  |
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| One Class Classification | |
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| Classification | |
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| Multiclass classification | |
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| Regression | |
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| Structured Output Learning | |
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| Pre-Processing | |
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| Built-in Model Selection Strategies | |
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| Visualization | |
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| Test Framework | |
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| Large Scale Learning | |
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| Semi-supervised Learning | |
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| Multitask Learning | |
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| Domain Adaptation | |
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| Serialization | |
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| Parallelized Code | |
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| Performance Measures (auROC etc) | |
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| Image Processing | |
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| Supported Operating Systems | Linux | |
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| Windows | |
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| Mac OSX | |
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| Other Unix | |
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| Language Bindings | Python | |
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| R | |
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| Matlab | |
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| Octave | |
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| C/C++ | |
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| Command Line | |
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| Java | |
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| C# | |
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| Lua | |
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| Ruby | |
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| SVM Solvers | SVMLight | |
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| LibSVM | |
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| SVM Ocas | |
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| LibLinear | |
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| BMRM | |
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| LaRank | |
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| SVMPegasos | |
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| SVM SGD | |
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| other | |
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| Regression | Kernel Ridge Regression | |
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| Support Vector Regression | |
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| Gaussian Processes | |
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| Relevance Vector Machine | |
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| Multiple Kernel Learning | MKL | |
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| q-norm MKL | |
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| multiclass MKL | |
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| Classifiers | Naive Bayes | |
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| Bayesian Networks | |
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| Multi Layer Perceptron | |
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| RBF Networks | |
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| Logistic Regression | |
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| LASSO | |
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| Decision Trees | |
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| k-NN | |
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| Gaussian Process Classification | |
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| Linear Classifiers | Linear Programming Machine | |
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| LDA | |
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| Distributions | Markov Chains | |
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| Hidden Markov Models | |
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| Dimension Reduction | PCA | |
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| Kernel PCA | |
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| Isomap | |
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| Multidimensional scaling | |
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| Sammon mapping | |
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| Locally Linear Embedding | |
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| Diffusion Map | |
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| Local Tangent Space Alignment | |
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| Laplacian Eigenmaps | |
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| Barnes-Hut t-SNE | |
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| Independent Component Analysis | FIXME | |
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| Kernels | Linear | |
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| Gaussian | |
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| Polynomial | |
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| String Kernels | |
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| Sigmoid Kernel | |
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| Kernel Normalizer | |
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| Feature Selection | Forward | |
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| Wrapper methods | |
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| Recursive Feature Selection | |
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| Missing Features | Mean value imputation | |
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| EM-based/model based imputation | |
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| Clustering | Hierarchical Clustering | |
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| k-means | |
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| Optimization | BFGS | |
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| conjugate gradient | |
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| gradient descent | |
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| bindings to CPLEX | |
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| bindings to Mosek | |
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| bindings to other solver | |
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| Structural Output Learning | Label Sequence Learning | |
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| Factor Graph Learning | |
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| SO-SGD | |
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| Latent SO-SVM | |
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| Supported File Formats | Binary | |
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| Arff | |
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| HDF5 | |
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| CSV | |
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| libSVM/ SVMLight format | |
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| Excel | |
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| Protobuf | |
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| Supported Data Types | Sparse Data Representation | |
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| Dense Matrices | |
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| Strings | |
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| Support for native (e.g. C) types (char, signed and unsigned int8, int16, int32, int64, float, double, long double) | |
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Shogun网站上的关于主流机器学习工具包的比较,布布扣,bubuko.com
原文：http://www.cnblogs.com/sxss/p/3633229.html