Q. What is the feature that doesn’t belongs to pattern classification in feeddorward neural networks?

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Q. What is the feature that doesn’t belongs to pattern mapping in feeddorward neural networks?

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

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

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

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

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

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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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