Q. The perceptron convergence theorem is applicable for what kind of data?

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Q. 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?

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Q. 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.

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Q. Convergence in perceptron learning takes place if and only if:

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Q. When line joining any two points in the set lies entirely in region enclosed by the set in M-dimensional space , then the set is known as?

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Q. Is it true that percentage of linearly separable functions will increase rapidly as dimension of input pattern space is increased?

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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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