KNN > SVM

I just finished fixing my K-nearest neighbor program and what do you know, its detection rate is consistently better than the svm. The results on the k-fold cross-validation testing where k=5 is:

KNN

(K=1)

1. 72% accuracy, # test images = 129
2. 82% accuracy, # test images = 129
3. 75% accuracy, # test images = 129
4. 74% accuracy, # test images = 128
5. 77% accuracy, # test images = 129

(K=3)

1. 79% accuracy, # test images = 129
2. 83% accuracy, # test images = 129
3. 77% accuracy, # test images = 129
4. 75% accuracy, # test images = 128
5. 77% accuracy, # test images = 129

(K=5)

1. 75% accuracy, # test images = 129
2. 86% accuracy, # test images = 129
3. 81% accuracy, # test images = 129
4. 73% accuracy, # test images = 128
5. 77% accuracy, # test images = 129

(K=7)

1. 79% accuracy, # test images = 129
2. 84% accuracy, # test images = 129
3. 76% accuracy, # test images = 129
4. 74% accuracy, # test images = 128
5. 79% accuracy, # test images = 129

SVM

1. 79% accuracy, # test images = 129
2. 65% accuracy, # test images = 129
3. 59% accuracy, # test images = 129
4. 62% accuracy, # test images = 128
5. 71% accuracy, # test images = 129

Personally, I find these results very interesting....