Edward-E-S-Wang/DMS-PSO-SVM

Dynamic Multi-Swarm Particle Swarm Optimization Support Vector Machine

30
/ 100
Emerging

This project helps scientists, marketers, or anyone building classification models fine-tune their Support Vector Machine (SVM) models. It takes your raw data and a specified SVM model, then intelligently searches for the best settings to improve accuracy and prevent overfitting. The output is an optimized SVM model ready for accurate predictions.

Use this if you need to optimize the 'C' and 'gamma' hyperparameters of an RBF-kernel SVM, especially for high-dimensional, small-sample, or nonlinear classification problems where manual tuning or simple grid search is inefficient.

Not ideal if your classification task does not involve Support Vector Machines, or if you prefer a simpler, less computationally intensive method for hyperparameter tuning on straightforward datasets.

predictive-modeling machine-learning data-classification hyperparameter-tuning biomedical-data-analysis
No License No Package No Dependents
Maintenance 10 / 25
Adoption 6 / 25
Maturity 7 / 25
Community 7 / 25

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Last pushed

Mar 11, 2026

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