Shogun (toolbox) - Supported Algorithms

Supported Algorithms

Currently Shogun supports the following algorithms:

• Support vector machines
• Dimensionality reduction algorithms, such as PCA, Kernel PCA, Locally Linear Embedding, Hessian Locally Linear Embedding, Local Tangent Space Alignment, Linear Local Tangent Space Alignment, Kernel Locally Linear Embedding, Kernel Local Tangent Space Alignment, Multidimensional Scaling, Isomap, Diffusion Maps, Laplacian Eigenmaps
• Online learning algorithms such as SGD-QN, Vowpal Wabbit
• Clustering algorithms: k-means and GMM
• Kernel Ridge Regression, Support Vector Regression
• Hidden Markov Models
• K-Nearest Neighbors
• Linear discriminant analysis
• Kernel Perceptrons.

Many different kernels are implemented, ranging from kernels for numerical data (such as gaussian or linear kernels) to kernels on special data (such as strings over certain alphabets). The currently implemented kernels for numeric data include:

• linear
• gaussian
• polynomial
• sigmoid kernels

The supported kernels for special data include:

• Spectrum
• Weighted Degree
• Weighted Degree with Shifts

The latter group of kernels allows processing of arbitrary sequences over fixed alphabets such as DNA sequences as well as whole e-mail texts.