Q. What are affine transformations?

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

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Q. 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?

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Q. Number of output cases depends on what factor?

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

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Q. What is the objective of perceptron learning?

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Q. On what factor the number of outputs depends?

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Q. In perceptron learning, what happens when input vector is correctly classified?

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Q. When two classes can be separated by a separate line, they are known as?

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

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Q. Two classes are said to be inseparable when?

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

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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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