Q. The update in weight vector in basic competitive learning can be represented by?

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Q. An instar can respond to a set of input vectors even if its not trained to capture the behaviour of the set?

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Q. The weight change in plain hebbian learning is?

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Q. What is the nature of weights in plain hebbian learning?

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Q. How can divergence be prevented?

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Q. By normalizing the weight at every stage can we prevent divergence?

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Q. What is ojas rule?

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Q. What is the other name of feedback layer in competitive neural networks?

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Q. What kind of feedbacks are given in competitive layer?

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Q. Generally how many kinds of pattern storage network exist?

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Q. In competitive learning, node with highest activation is the winner, is it true?

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Q. What kind of learning is involved in pattern clustering task?

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Q. In pattern clustering, does physical location of a unit relative to other unit has any significance?

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Q. How is feature mapping network distinct from competitive learning network?

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Q. What is the objective of feature maps?

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Q. How are weights updated in feature maps?

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Q. In feature maps, when weights are updated for winning unit and its neighbour, which type learning it is known as?

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Q. In self organizing network, how is layer connected to output layer?

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Q. What is true regarding adaline learning algorithm?

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Q. What is true for competitive learning?

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