Question 1
Marks : +2 | -2
Pass Ratio : 100%
Two classes are said to be inseparable when?
Explanation: Linearly separable classes, functions can be separated by a line.
Question 2
Marks : +2 | -2
Pass Ratio : 100%
In perceptron learning, what happens when input vector is correctly classified?
Explanation: No adjustments in weight is done, since input has been correctly classified which is the objective of the system.
Question 5
Marks : +2 | -2
Pass Ratio : 100%
If e(m) denotes error for correction of weight then what is formula for error in perceptron learning model: w(m + 1) = w(m) + n(b(m) – s(m)) a(m), where b(m) is desired output, s(m) is actual output, a(m) is input vector and ‘w’ denotes weight
Explanation: Error is difference between desired and actual output.
Question 8
Marks : +2 | -2
Pass Ratio : 100%
When two classes can be separated by a separate line, they are known as?
Explanation: Linearly separable classes, functions can be separated by a line.
Question 10
Marks : +2 | -2
Pass Ratio : 100%
w(m + 1) = w(m) + n(b(m) – s(m)) a(m), where b(m) is desired output, s(m) is actual output, a(m) is input vector and ‘w’ denotes weight, can this model be used for perceptron learning?
Explanation: Gradient descent can be used as perceptron learning.