Thread: image analysis theory: mapping a network

Lets say you have a set of images and a set of image features, the image features are a huge set of various statistics performed on the image. You are trying to use image features to classify these images, and these image features may be inter-related. We are essentially trying to produce a graph that shows that these features are dependant (positively or negatively) with one another.

Lets say there are 5 image types, and 1000 features.
Lets say you had enough data to calculate how each feature was related to the type of image. Say, the image analysis algorithm is represented by a formula R(f) for f=(1 ... 1000), but features can be corelated. What strategies are there to produce the neural network graph for problem? See the following diagram:

Code:

http://upload.wikimedia.org/wikipedia/commons/e/e4/Artificial_neural_network.svg

Is there a particular mathematical field that deals with this? What is it called? Combinatronics?
Hope this makes some sense...

Is that your rug-rat, brewbuck? Cute kid. I know that is off topic, but I have a short attention span.

I bet life was a little less stressful back when he wasn't walking yet. I can't tell if he has a book or a keyboard. I am leaning towards keyboard.

weka is in java. What do you think of R statistical programming language (its based on S)?