HSSVM

Features

• 1. Normalize sample data
• 2. Search optimal C-gamma pairs for further training and prediction
• 3. Train sample data and build hyper-sphere classification model
• 4. Predict unknown-class sample data by using hyper-sphere model gotten from training process
• 5. Provide cache for kernel-matrix to train or predict large-scale sample data.
• 6. Provide simple commands to run all processes
• 7. Count the executive time of all operations above
• 8.Check if there are any errors in data file;

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