Q. To provide generalization capability to a network, what should be done?

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Q. Does an approximate system produce strictly an interpolated output?

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Q. The nature of mapping problem decides?

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Q. How is hard learning problem solved?

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Q. The number of units in hidden layers depends on?

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Q. From given input-output pairs pattern recognition model should capture characteristics of the system?

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Q. Let a(l), b(l) represent in input-output pairs, where “l” varies in natural range of no.s, then if a(l)=b(l)?

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Q. Let a(l), b(l) represent in input-output pairs, where “l” varies in natural range of no.s, then if a(l)=!b(l)?

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Q. The recalled output in pattern association problem depends on?

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Q. If a(l) gives output b(l) and a’=a(l)+m,where m is small quantity and if a’ gives ouput b(l) then?

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Q. If a(l) gives output b(l) and a’=a(l)+m,where m is small quantity and if a’ gives ouput b(l)+n then?

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Q. Can system be both interpolative and accretive at same time?

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Q. What are 3 basic types of neural nets that form basic functional units?

i)feedforward
ii) loop
iii) recurrent
iv) feedback
v) combination of feed forward and back

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Q. Feedback networks are used for autoassociation and pattern storage?

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Q. Feedforward networks are also used for autoassociation and pattern storage?

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Q. What is the objective of backpropagation algorithm?

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Q. The backpropagation law is also known as generalized delta rule, is it true?

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Q. What is true regarding backpropagation rule?

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Q. There is feedback in final stage of backpropagation algorithm?

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Q. What is meant by generalized in statement “backpropagation is a generalized delta rule” ?

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