Question

Which of the following are true when comparing ANNs and SVMs?
A) ANN error surface has multiple local minima while SVM error surface has only one minima
B) After training, an ANN might land on a different minimum each time, when initialized with random weights during each run.
C) In training, ANN's error surface is navigated using a gradient descent technique while SVM's error surface is navigated using convex optimization solvers.
D) As shown for Perceptron, there are some classes of functions that cannot be learnt by an ANN. An SVM can learn a hyperplane for any kind of distribution.

Answer 1

answer is a,b,d.this is what i wrote

Answer 2

Answer:

When comparing to ANNs and SVMs Option A, B, D are correct.

Explanation:

A) ANN error surface has multiple local minima while the SVM error surface has only one minimum: True.

B) After training, an ANN might land on a different minimum each time, when initialized with random weights during each run: True

C) In training, ANN's error surface is navigated using a gradient descent technique while SVM's error surface is navigated using convex optimization solvers: True

D) As shown for Perceptron, there "are some classes of functions that can't be learned by an ANN". An SVM can learn a hyperplane for any kind of distribution: False.

SVN stands for support vector machines and ANN stands for artificial neural network and both are the components of machine intelligence i.e artificial intelligence.