Learning from Noise

Because the data consisted of long records of real values, the student was advised to use artificial neural networks. After several weeks of producing random classifiers, the student showed up at my office and asked whether I could help. It always seems a good idea to analyze the data first, so we constructed a primitive visualization: signal strength of four antennae over time. The graphs looked like we'd glued a pen on a dog's tail while showing him a juicy T-bone steak. I suggested we add a few functions, such as pairwise difference, mean, deviation, and so on--just to get a feel for the data.