I'm trying to predict whether a drawing matches a "trained" drawing. The drawings are black on white background images (32 x 32). More specifically I'd like to check if a new example is relatively close enough to the trained examples or completely different. The trained drawings would be different for each user that tests this.
The requirement that I've set for myself is that I only have 3 training examples of what is the drawing. What are some recommended learning algorithms that work with little data, and can help in this scenario?
Here's examples of some training data:
What I've tried:
One-Class SVM with non-linear kernel (Novelty Detection)
Specifically I used RBF kernel with values 0.1 for nu and gamma. As you can see from the graph below it classifies anomalies and non-anomalous test examples as acceptable, except I don't know how to split the data between the red and the green.
I also tried using a sigmoid kernel, but as you can see it now misses everything:
I've also used Logistic Regression using a regularization parameter of 100. I fed it 3 negative classes that I made (Maybe not the best idea since I don't know what their drawing will be in advance:
At the most it gets as good as 75% accuracy and at worst (blank drawing) 63%. Precision: 0.666667 | Recall: 1.000000 | F-Score: 0.800000.
I've heard of boosting a class, but I'm wondering how do I do that when I have only one class (positive examples)?