Question 4
Which of the following statements best describes the primary representational advantage of a Multi-Layer Perceptron (MLP) over a Single-Layer Perceptron (SLP)?
An MLP can learn non-linear decision boundaries, allowing it to classify datathat is not linearly separable.
An MLP converges faster than an SLP on linearly separable data because itshidden layers accelerate learning.
An MLP is less prone to overfitting than an SLP because it has moreparameters to capture the data distribution.
An MLP can only be used for classification tasks, whereas an SLP can be usedfor both classification and regression.