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> ML_LITERATURE // CHANG-2011-LIBSVM-LIBRARY-FOR-SUPPORT-VECTOR-MACHINES_v1.0

LIBSVM: A Library for Support Vector Machines

Chih-Chung Chang, Chih-Jen Lin · ACM Transactions on Intelligent Systems and Technology (TIST) (2011)

systems2011industry-standardthirdPartyReproduced
DOI: 10.1145/1961189.1961199Back to Literature

Principal Contribution

Implemented the Sequential Minimal Optimization (SMO) algorithm for kernel SVMs, C-SVC, nu-SVC, epsilon-SVR, and one-class SVM with probability estimates.

Operational Relevance

Serves as canonical technical reference for implementing task-binary-classification, task-multiclass-classification, task-regression, task-anomaly-detection 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:
Support Vector Machines (SVM)One-Class Support Vector Machine (OC-SVM)
Related Architectures:
Implementing Libraries: