Which of the following is/are true about the Perceptron classifier? A) It can learn a OR function B) It can learn a AND function C) The obtained separating hyperplane depends on the order in which the points are presented in the training process. D) For a linearly separable problem, there exists some initialization of the weights which might lead to non-convergent cases.
Answers
A, B, C are correct
A) It can learn a OR function
B) It can learn a AND function
C) The obtained separating hyperplane depends on the order in which the points are presented in the training process.
Answer:
A, B, C are true about the Perceptron classifier.
It can learn a OR function ,It can learn a AND function , The obtained separating hyperplane depends on the order in which the points are presented in the training process is the Perceptron classifier.
Explanation:
A) It has the ability to learn an OR function.
B) It has the ability to learn the AND function.
C) The separating hyperplane that is obtained is determined by the order in which the points are presented during the training process.
a, b, and c are the possible solutions.
Because OR is a linear function, it can be learned using a perceptron.
- The XOR function is a nonlinear function that cannot be learned using a perceptron learning algorithm.
- can only learn linear functions
- The order in which the data is presented affects the perceptron learning algorithm.
- There are multiple possible hyperplanes, and we will converge to any of them depending on the order.
We can also show that if a separating hyperplane exists, the algorithm always converges to it. For a linearly separable problem, there exists some initialization of the weights which might lead to non-convergent cases.
As a result, d is false.