I'm working on a pattern recognition problem. I have been using supervised learning (neural network and svm with one class classification) but I think I'm doing it in a wrong way. For simplifying, the problem I'm going to describe below is just an example.
In order to determine the pattern (called pattern X), I have the following training data (4 features to determine pattern X):
And this is my testing data: 0,1,3,5,X ?
As you can see, the first two numbers only accept binary numbers, the third number only accepts even numbers and the fourth number only accepts odd numbers.
With neural network: I think this problem is not suitable for neural network because I only have true values. And the neural network should be trained by both true and false value. Or should it be other way around ?
With svm one classification: Currently I'm using libsvm library and got accuracy at 0%, I don't know should this be problem from training data or not...
So should I change to unsupervised learning in order to find the pattern in the given training data?