SHOGUN
SHOGUN is a sophisticated machine learning toolbox designed for large-scale kernel methods, emphasizing Support Vector Machines (SVM). It features a versatile SVM object compatible with various implementations, including state-of-the-art options like OCAS and LibSVM. The toolbox supports multiple kernels, including recent string kernels, and allows for on-the-fly preprocessing and custom kernel configurations, facilitating complex classification and regression tasks across diverse data types.
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Top SHOGUN Features
- Large scale kernel methods
- Generic SVM object
- State of the art implementations
- Multiple kernel learning support
- On-the-fly feature space computing
- Custom pre-computed kernels
- Combined kernel creation
- Efficient string kernels
- Support for dense and sparse data
- Chains of preprocessors attachment
- Multiple programming language interfaces
- Extensive documentation and examples
- Active community and support
- GitHub issue tracker
- IRC chat support
- Open source software
- High performance for large datasets
- Bioinformatics application examples
- Regression and classification capabilities
- Comprehensive kernel optimization techniques.
