MCQOPTIONS
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MCQOPTIONS
This section includes 10 Mcqs, each offering curated multiple-choice questions to sharpen your Neural Networks knowledge and support exam preparation. Choose a topic below to get started.
| 1. |
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 |
| A. | e(m) = n(b(m) – s(m)) a(m) |
| B. | e(m) = n(b(m) – s(m)) |
| C. | e(m) = (b(m) – s(m)) |
| D. | none of the mentioned |
| Answer» D. none of the mentioned | |
| 2. |
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? |
| A. | yes |
| B. | no |
| Answer» B. no | |
| 3. |
The perceptron convergence theorem is applicable for what kind of data? |
| A. | binary |
| B. | bipolar |
| C. | both binary and bipolar |
| D. | none of the mentioned |
| Answer» D. none of the mentioned | |
| 4. |
Is it necessary to set initial weights in prceptron convergence theorem to zero? |
| A. | yes |
| B. | no |
| Answer» C. | |
| 5. |
Two classes are said to be inseparable when? |
| A. | there may exist straight lines that doesn’t touch each other |
| B. | there may exist straight lines that can touch each other |
| C. | there is only one straight line that separates them |
| D. | all of the mentioned |
| Answer» D. all of the mentioned | |
| 6. |
If two classes are linearly inseparable, can perceptron convergence theorem be applied? |
| A. | yes |
| B. | no |
| Answer» C. | |
| 7. |
When two classes can be separated by a separate line, they are known as? |
| A. | linearly separable |
| B. | linearly inseparable classes |
| C. | may be separable or inseparable, it depends on system |
| D. | none of the mentioned |
| Answer» B. linearly inseparable classes | |
| 8. |
In perceptron learning, what happens when input vector is correctly classified? |
| A. | small adjustments in weight is done |
| B. | large adjustments in weight is done |
| C. | no adjustments in weight is done |
| D. | weight adjustments doesn’t depend on classification of input vector |
| Answer» D. weight adjustments doesn’t depend on classification of input vector | |
| 9. |
On what factor the number of outputs depends? |
| A. | distinct inputs |
| B. | distinct classes |
| C. | both on distinct classes & inputs |
| D. | none of the mentioned |
| Answer» C. both on distinct classes & inputs | |
| 10. |
What is the objective of perceptron learning? |
| A. | class identification |
| B. | weight adjustment |
| C. | adjust weight along with class identification |
| D. | none of the mentioned |
| Answer» D. none of the mentioned | |