This is Shark 3.0 beta. See the news for more information.
SHARK is a fast, modular, feature-rich open-source C++ machine learning library.
It provides methods for linear and nonlinear optimization, kernel-based learning
algorithms, neural networks, and various other machine learning techniques (see the
feature list below).
It serves as a powerful toolbox for real world applications as well as research.
Shark depends on Boost and CMake.
It is compatible with Windows, Solaris, MacOS X, and Linux. Shark is licensed under
GNU Lesser General Public License.
For an overview over the previous major release of Shark (2.0) we
Christian Igel, Verena Heidrich-Meisner, and Tobias Glasmachers.
Journal of Machine Learning Research 9, pp. 993-996, 2008.
Where to start
In the menu above, click on “Getting started”, or use this direct link to the
After installation, there is a guide to the different documentation pages available
Speed and flexibility
Shark provides an excellent trade-off between flexibility and
ease-of-use on the one hand, and computational efficiency on the other.
One for all
Shark offers numerous algorithms from various machine learning and
computational intelligence domains in a way that they can be easily
combined and extended.
Shark comes with a lot of powerful algorithms that are to our best
knowledge not implemented in any other library, for example in the
domains of model selection and training of binary and multi-class SVMs,
or evolutionary single- and multi-objective optimization.
Shark currently supports:
- Supervised learning
- Linear discriminant analysis (LDA), Fisher–LDA
- Naive Bayes classifier (supporting generic distributions)
- Linear regression
- Support vector machines (SVMs) for one-class, binary and true
multi-category classification as well as regression; includes fast variants for linear kernels.
- Feed-forward and recurrent multi-layer artificial neural networks
- Radial basis function networks
- Regularization networks as well as Gaussian processes for regression
- Iterative nearest neighbor classification and regression
- Decision trees and random forests
- Unsupervised learning
- Principal component analysis
- Restricted Boltzmann machines (including many state-of-the-art
- Hierarchical clustering
- Data structures for efficient distance-based clustering
- Evolutionary algorithms
- Single-objective optimization (e.g., CMA–ES)
- Multi-objective optimization (in particular, highly efficient
algorithms for computing as well as approximating the contributing hypervolume)
- Basic linear algebra and optimization algorithms