Edward-E-S-Wang/DMS-PSO-SVM
Dynamic Multi-Swarm Particle Swarm Optimization Support Vector Machine
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.
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24
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2
Language
Jupyter Notebook
License
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Category
Last pushed
Mar 11, 2026
Commits (30d)
0
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