Q. If pattern classes are linearly separable then hypersurfaces reduces to straight lines?

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Q. As dimensionality of input vector increases, what happens to linear separability?

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Q. In a three layer network, shape of dividing surface is determined by?

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Q. In a three layer network, number of classes is determined by?

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Q. Intersection of linear hyperplanes in three layer network can only produce convex surfaces, is the statement true?

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Q. Intersection of convex regions in three layer network can only produce convex surfaces, is the statement true?

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Q. If the output produces nonconvex regions, then how many layered neural is required at minimum?

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Q. Can all hard problems be handled by a multilayer feedforward neural network, with nonlinear units?

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Q. What is a mapping problem?

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Q. Can mapping problem be a more general case of pattern classification problem?

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Q. What is the objective of pattern mapping problem?

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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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