Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results.

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Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results.

1. true – this works always, and these multiple perceptrons learn to classify even complex problems

2.false – perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do

3. true – perceptrons can do this but are unable to learn to do it – they have to be explicitly hand-coded

4.alse – just having a single perceptron is enough

Posted Date:-2022-06-22 09:25:42

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  3. In which neural net architecture, does weight sharing occur?
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  6. Which of the following is an application of NN (Neural Network)?
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  8. Which one of the following is not a major strength of the neural network approach?
  9. Which Statement is not true statement.
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  17. Hierarchical clustering is slower than non-hierarchical clustering?
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