Delta Training Rule Summary
 Math/Machine Learning Terminology
 Rule Post Pruning Summary
 FindS Summary
 Candidate Elimination Summary
 Decision Trees Summary
 Stochastic Gradient Descent Summary
 Delta Training Rule Summary
 Perceptron Training Rule Summary
 A Summary Of Multilayer Neural Networks
 Abdullah's Machine Learning Notes
Delta Training Rule Algorithm is a machine learning algorithm to learn the weights of a single neuron (unit).
Click here to learn what a unit is. At the very least, read the section on what a unit is, but I recommend you read the whole thing.
Here is the algorithm:
 arbitrarily initialize

set error function to something like:
where
 is a particular training example
 is the actual output for that training example (in this case +1 or 1)
 is the output as predicted by our current weights
 do gradient descent (or stochastic gradient descent) to find
Delta Rule vs Perceptron Rule
Both of these rules can be used to find . What are the pros/cons of each?
perceptron rule  delta rule 

requires linearly separable data  does not 
can perfectly classify training examples in finite steps  asymptotically approaches perfect classification 