Q. In case of pattern by feedback nets in pattern recognition task, what is the behaviour expected?

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Q. What are hard problems?

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Q. In order to overcome constraint of linearly separablity concept of multilayer feedforward net is proposed?

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Q. The hard learning problem is ultimately solved by hoff’s algorithm?

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Q. Generalization feature of a multilayer feedforward network depends on factors?

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Q. What is accretive behaviour?

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Q. What is Interpolative behaviour?

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Q. Does pattern association involves non linear units in feedforward neural network?

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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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