How is hard learning problem solved?

Neural Networks - Quiz(MCQ)

using nonlinear differentiable output function for output layers

using nonlinear differentiable output function for output and hidden layers

using nonlinear differentiable output function for hidden layers

it cannot be solved

Correct Answer : using nonlinear differentiable output function for output and hidden layers

Explanation : Hard learning problem is solved by using nonlinear differentiable output function for output and hidden layers.

Recently Updated in Neural Networks Questions

A greater value of 'p' the vigilance parameter leads to?

No change

Bigger clusters

Small clusters

None of the above

Correct Answer : Small clusters

Explanation : Input samples associated with same neuron get reduced.

What type of inputs does ART â€“ 1 receives?

binary

bipolar

both bipolar and binary

none of the above

Correct Answer : binary

Explanation : Adaptive resonance theory take care of stability plasticity dilemma.

What does vigilance parameter in ART determines?

Number of desired outputs

Number of possible outputs

Number of acceptable inputs

None of the above

Correct Answer : None of the above

Explanation : Vigilance parameter in ART determines the tolerance of matching process.

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