Question

Following training samples are given: | x1 | x2 | Class | | :--- | :--- | :--- | | 1 | 1 | +1 | | -1 | -1 | -1 | | 0 | 0.5 | -1 | | 0.1 | 0.5 | -1 | | 0.2 | 0.2 | +1 | | 0.9 | 0.5 | +1 | Table 1: Sample data Assuming weight vector of initial decision boundary w^T x = 0 as w=[1, 1], solve the following: 1. In how many steps perception learning algorithm will converge. (10 points) 2. What will be the final decision boundary? Show step-wise-step update of weight vector using computation as well as hand-drawn plot. (10 points) 3. Prove that Perceptron Learning Algorithm converges in a finite number of steps. (10 points)

          Following training samples are given:

| x1 | x2 | Class |
| :--- | :--- | :--- |
| 1 | 1 | +1 |
| -1 | -1 | -1 |
| 0 | 0.5 | -1 |
| 0.1 | 0.5 | -1 |
| 0.2 | 0.2 | +1 |
| 0.9 | 0.5 | +1 |

Table 1: Sample data

Assuming weight vector of initial decision boundary w^T x = 0 as w=[1, 1], solve the following:

1. In how many steps perception learning algorithm will converge. (10 points)
2. What will be the final decision boundary? Show step-wise-step update of weight vector using computation as well as hand-drawn plot. (10 points)
3. Prove that Perceptron Learning Algorithm converges in a finite number of steps. (10 points)

Following training samples are given:

| x1 | x2 | Class |
| :— | :— | :— |
| 1 | 1 | +1 |
| -1 | -1 | -1 |
| 0 | 0.5 | -1 |
| 0.1 | 0.5 | -1 |
| 0.2 | 0.2 | +1 |
| 0.9 | 0.5 | +1 |

Table 1: Sample data

Assuming weight vector of initial decision boundary w^T x = 0 as w=[1, 1], solve the following:

1. In how many steps perception learning algorithm will converge. (10 points)
2. What will be the final decision boundary? Show step-wise-step update of weight vector using computation as well as hand-drawn plot. (10 points)
3. Prove that Perceptron Learning Algorithm converges in a finite number of steps. (10 points)

Added by John B.

Question

Please give Ace some feedback