cjlin1/libsvm

Health v2 · maintenance only

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4708 stars · 1636 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

LIBSVM is a widely used library and command-line toolset for support vector machine classification, regression, and distribution estimation, with C/C++, Java, Python, and MATLAB/OCTAVE interfaces. It includes svm-train, svm-predict, and svm-scale tools plus parameter selection utilities like easy.py and grid.py.

Use cases

• train an SVM classifier on labeled data
• do support vector regression for prediction
• run one-class SVM for anomaly detection
• perform multi-class classification
• select SVM hyperparameters with cross-validation grid search
• scale sparse feature data before training

When to choose

• you need a proven, efficient SVM implementation with multi-language bindings
• your dataset is small to medium sized and fits in memory
• you want sparse data format support and precomputed kernels
• you need a stable, well-documented classic ML library with BSD license

When to avoid

• you need deep learning or GPU-accelerated training
• your dataset is very large and SVM training does not scale well
• you want gradient-boosted trees or other modern ML algorithms
• you need an end-to-end ML pipeline framework rather than a single algorithm

Facets

library · maturity stable

machine-learning cli sdk machine-learning data-science cross-platform cli python jvm cpp windows support-vector-machines svm classification regression sparse-data matlab-interface algorithms linux macos

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