That's the problem$$y=(x,w,\rho) = \begin{cases} 1 & \sum_{i=1}^3 w_ix_i >\rho\\ 0 & \text{otherwise}\end{cases},$$where $x=\{x_1,x_2,x_3\}$ are inputs, $w=\{w_1,w_2,w_3\}$ are weights and$\rho$ is the threshold value.The problem asks to find an opportune set of weights that can classify these inputs.$$A = \{(1, 2, 0), (1, 3, 0), (2, 3, 0)\},$$ $$B = \{(0,1,2),(9,0,1),(3,3,3)\}.$$The first step is to assign a random weights to all inputs$w_1= 0.5$, $w_2= 0.7$, $w_3=0.3$.For the first example$(1,2,0)$ that I know is part of the A class$\sum_{i=1}^3 w_ix_i=1.9 < \rho~ \Rightarrow y=0$ is the result.I need to update the weights but I can't understand how.The formula is$w_i'=w_i*n*x(t-y)$.Correct?
Perceptron learning rule for classification
artificial intelligence;probability theory;neural networks;classification