A few days ago I asked the question, if a NN with linear activation function can produce a function concatenated of linear functions, what actually is impossible (Can a NN with linear activation functions produce a connection of linear functions?).
Now I have here some classification examples, but I really cannot perfectly decide, which one is based on which approach:
1 -> C The perceptron does not look for the maximum separation margin.
2 -> E Neural network with linear activation function
3 -> A Linear SVM, because of the maximum separation margin.
4 -> B Because of the hyperbolic shape of the hyperplane.
5 -> D? Logisitc regression? I tought it can only linear separate?
6 -> F I guess the NN with tanh activation function, because of the no very smooth shape, which comes from the too small hidden layer size.
I actually don't get how the logistic regression classifier should be able to produce a hyperplane like in 5? What did I classify wrong here?