The original perceptron, proposed by Frank Rosenblatt in 1958, is a simple single-layer binary classifier. It learns by adjusting weights based on errors in prediction. The output is computed as: $$ y = \begin{cases} 1 & \text{if } \sum_{i=1}^{n} w_i x_i + b \geq 0 \\ 0 & \text{otherwise} \end{cases} $$ During training, weights are updated as: $$ w_i := w_i + \eta (y_{true} - y_{pred}) x_i $$ Where: - \( x_i \): input feature - \( w_i \): weight - \( b \): bias - \( \eta \): learning rate - \( y_{true} \): actual label - \( y_{pred} \): predicted output Which of the following best describes the behavior and training method of the original perceptron? {blank1}