Description:

SALSA (Software lab for Advanced machine Learning with Stochastic Algorithms) is a native Julia implementation of the well-known stochastic algorithms for Regularized Empirical Risk minimization and sparse linear modelling. It aims at bridging a gap between sophisticated machine learning concepts and user-friendly environment with API which guides the user through many intermediate selection steps. The API features low- and high-level routines for both an experienced and a novice user. SALSA is managed and versioned by the package manager embedded in Julia.

The SALSA software package can be used as a framework and a library. As a framework it embraces all steps of creating a valid ML model, such as cross-validation and hyperparameter tuning. As a library it can be used for embedding separate core algorithmic and preprocessing routines into other frameworks as well. Nice interoperability between the Julia eco-system and other languages (such as C/C++, Python etc.) ensures seamless and quick integration.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: Ascii, Any Format Supported By Matlab, Libsvm Format, Multiple Representations Format

Tags: Classification, Clustering, Regression, Support Vector Machines, Online Learning, Stochastic Gradient Descent, Machine Learning, Optimization, Algorithms, Supervised Learning, Sparse Learning, Sparsity, Regularization, Prediction, Online Multiclass Classification, Sparse Data, Statistical Learning, Machine Learning Toolbox, Stochastic Optimization

Archive: download here

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