1. In determination of weights by learning, for orthogonal input vectors what kind of learning should be employed?

a) hebb learning law

b) widrow learning law

c) hoff learning law

d) no learning law

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2. In determination of weights by learning, for linear input vectors what kind of learning should be employed?

a) hebb learning law

b) widrow learning law

c) hoff learning law

d) no learning law

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3. In determination of weights by learning, for noisy input vectors what kind of learning should be employed?

a) hebb learning law

b) widrow learning law

c) hoff learning law

d) no learning law

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4. What are the features that can be accomplished using affine transformations?

a) arbitrary rotation

b) scaling

c) translation

d) all of the mentioned

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5. What is the features that cannot be accomplished earlier without affine transformations?

a) arbitrary rotation

b) scaling

c) translation

d) all of the mentioned

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6. what are affine transformations?

a) addition of bias term (-1) which results in arbitrary rotation, scaling, translation of input pattern.

b) addition of bias term (+1) which results in arbitrary rotation, scaling, translation of input pattern.

c) addition of bias term (-1) or (+1) which results in arbitrary rotation, scaling, translation of input pattern.

b) none of the mentioned

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7. Can a artificial neural network capture association if input patterns is greater then dimensionality of input vectors?

a) yes

b) no

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8. By using only linear processing units in output layer, can a artificial neural network capture association if input patterns is greater then dimensionality of input vectors?

a) yes

b) no

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9. Number of output cases depends on what factor?

a) number of inputs

b) number of distinct classes

c) total number of classes

d) none of the mentioned

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10. For noisy input vectors, Hebb methodology of learning can be employed?

a) yes

b) no

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