2013/03/05 by Andrea Tacchetti, Pavan Kumar Mallapragada, Tacchetti, Andrea +5 · 1 citation
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Mathematical Software (cs.MS) #Neural Networks and Applications #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.1303.0934
openalex publication_date 2013/03/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present GURLS, a least squares, modular, easy-to-extend software library for efficient supervised learning. GURLS is targeted to machine learning practitioners, as well as non-specialists. It offers a number state-of-the-art training strategies for medium and large-scale learning, and routines for efficient model selection. The library is particularly well suited for multi-output problems (multi-category/multi-label). GURLS is currently available in two independent implementations: Matlab and C++. It takes advantage of the favorable properties of regularized least squares algorithm to exploit advanced tools in linear algebra. Routines to handle computations with very large matrices by means of memory-mapped storage and distributed task execution are available. The package is distributed under the BSD licence and is available for download at https://github.com/CBCL/GURLS.