Description:

RLScore is a machine learning software package for regularized kernel methods, focusing especially on Regularized Least-Squares (RLS) based methods. The main advantage of the RLS family of methods is that they admit a closed form solution, expressed as a system of linear equations. This allows deriving highly efficient algorithms for RLS methods, based on matrix algebraic optimization. Classical results include computational short-cuts for multi-target learning, fast regularization path and leave-one-out cross-validation. RLScore takes these results further by implementing a wide variety of additional computational shortcuts for different types of cross-validation strategies, single- and multi-target feature selection, multi-task and zero-shot learning with Kronecker kernels, ranking, stochastic hill climbing based clustering etc. The majority of the implemented methods are such that are not available in any other software package.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Supported Operating Systems: Linux, Windows

Data Formats: Agnostic, Numpy

Tags: Classification, Regression, Kernel Methods, Feature Selection, Ranking, Tensor, Cross Validation, Kronecker Product Kernels, Pair Input Learning, Pairwise Learning

Archive: download here

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