This project implements the SMO algorithm for SVM in Python.

Author: Soloice.

Here are some instructions for the project:

All source codes are in the folder 'src2/'. Two classes BinarySVM and MultiSVM are defined in the file svm.py. demo_test.py, multi_test.py and svm_test.py all used to debug the SMO algorithm: demo_test.py includes a data generator which generates 2-dimensional linear separable/almost-separable/circular data of 2 classes, then visualize the data points and train a BinarySVM. Similarly, multi_test.py serves for testing MultiSVM. In svm_test.py, some real data are extracted from the MNIST dataset and are visualized using the PCA technique. Finally, svm_test_full.py trains a SVM classifier on the whole MNIST data.

In my experiment, I found training an SVM with 'RBF' kernel is much faster than that with linear kernel. I don't why. Perhaps it is because in RKHS the data points are more separable thus facilitates the training procedure. For your reference, Training a MultiSVM classifier with 'RBF' kernel on 6/7 MNIST data (i.e., using 60k examples as the training set) takes 11462s on my workstation (32GB RAM, 1 CPU with 8 Intel(R) Xeon(R) CPU E5-1620 v2 @ 3.70GHz cores.)

Overall, the results can be summarized as follows: