Calculate the error using a sigmoid function in backpropagation

Question

I have a quick question regarding backpropagation. I am looking at the following:

http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf

In this paper, it says to

Answer 1

The choice of the sigmoid function is by no means arbitrary. Basically you are trying to estimate the conditional probability of a class label given some sample. If you take the absolute value, you are doing something different, and you will get different results.

For a practical introduction in the topic I would recommend you to check out the online Machine Learning course by Prof. Andrew Ng

https://www.coursera.org/course/ml

and the book by Prof. Christopher Bishop for an in depth study on the topic

http://www.amazon.com/Neural-Networks-Pattern-Recognition-Christopher/dp/0198538642/ref=sr_1_1?ie=UTF8&qid=1343123246&sr=8-1&keywords=christopher+bishop+neural+networks