Quote:
Originally Posted by legolas_sid
Hi all,
it would be great if any of you could tell me if my understanding of this algorithm is correct.
1)pick a target function(f)(i.e a line in the [1,1] * [1,1] plane)
2)generate a set of training examples of the form ((x_d,y_d),output)
(I am confused from this point onwards,please correct me if I am wrong )
3)we initially set H as a vector of 0.0s
4)apply the perceptron iteration till the h function 'linearly seperates' all the data points.(we call this g).
5)test this g on a set of new data points (test_data,f(test_data)).
6)compare this with h and calculate error probability.

You are correct, with step 3 put more accurately as "we start with
that corresponds to a weight vector of 0's." In step 1, the problem specifies a particular way of picking the random line that will define the target function.