1. 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

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2. 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

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3. 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

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4. 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

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5. If two classes are linearly inseparable, can perceptron convergence theorem be applied?

a) yes

b) no

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6. 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

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7. Is it necessary to set initial weights in prceptron convergence theorem to zero?

a) yes

b) no

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8. 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

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9. 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

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10. 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

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