I am trying to compute the accuracy of the HMAX model.
I am using the
Face category (containing 435 images) from the Caltech101 database. I split it into $x$ training and $y$ testing. At each time, when $x$ increases, the accuracy also increases. Furthermore, I heard that the number of training should be equal to 80% by comparing it to the tests. So when I split my data into 348 positive training and the rest for positive testing, I got an accuracy that it is smaller than the other smaller splits (when $x<348)$!
By the way, I also used the
Background category and I split it into 50 negative training and 50 negative testing.
Why do I get smaller accuracy?