SVM-Python
By Dongfeng Gu (https://www.gdf.name)
Introduction
This is a basic implementation of a soft-margin kernel SVM solver in Python using numpy and cvxopt.
Usage
Training:
1. Go to the bin/svm-train, train the dataset first by passing the dimension of the X features and the directory of the dataset
2. run the program and it will generate a model.txt which will be used in the testing part
Testing:
1. Go to the bin/svm-test, test the dataset first by passing the dimension of the X features and the directory of the dataset, it will automatically search for the model.txt file.
- run the program and it will generate the error rate