> ML_LITERATURE // FAN-2008-LIBLINEAR-LIBRARY-FOR-LARGE-LINEAR-CLASSIFICATION_v1.0
LIBLINEAR: A Library for Large Linear Classification
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin · Journal of Machine Learning Research (JMLR) (2008)
systems2008industry-standardthirdPartyReproduced
Principal Contribution
Engineered coordinate descent solvers for L1/L2 regularized linear SVMs and logistic regression, scaling linear models to millions of instances.
Operational Relevance
Serves as canonical technical reference for implementing task-binary-classification, task-multiclass-classification, task-regression in production systems.
Assumptions
- Underlying data distribution satisfies empirical consistency and regularity assumptions across training domains
Limitations
- Scaling characteristics and accuracy depend on hardware architecture, parameter scale, and dataset quality
Connected Algorithms, Architectures & Tools
Related Algorithms:
Related Architectures:
Implementing Libraries: