I have a question regarding multi-layer perceptron in neural network. So basically as can be seen from the image below, it is an input space where every pattern inside the triangle has target output O = 1 and patterns outside the triangle O = 0.
Given the following input patterns and their target outputs:
mu | x1 | x2 | O 1 0 -2 0 2 0 1 1 3 1 1 1 4 -3 4 0 5 4 4 0
we try to solve this classification problem by using a neural network with 2 input, 3 hidden neurons and one output.
How can I find the weights and thresholds of the decision boundaries? From my experience, weights are perpendicular with the decision boundary and thresholds are how much the decision boundaries shift from origin, But using these assumption, I keep getting the classification wrong for some patterns.
Thanks in advance