rlscore 0.7

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.