I want to say ahead that I highly appreciate any Literature recommendation (Book, blogg, ...) besides ESL and ISL.
My Question: Is it possible to train a 3 layer multilayer perceptron (mlp) in a binary classification problem with just 135 observations with out massive over fitting?
I know this is a complex topic and the required size depends on many factors, and there is most certainly not just one right answer.
However I read as a rule of thumb: At least 10 observations / per parameter (in case of mlp: weights), can somebody confirm this rule?
Details to the data:
N = 135
M = 20 Features = 20 Input Neurons
J = Neuron ins hidden layer = ??
Y / O = binary class = 2 Neurons
Parameters/Weights So for instance if we assume just 5 neurons in the hidden (J = 5, which is imo probably to low), this would mean we are searching for 110 Paramters
M*J + J * O = 20*5 + 5*2 = 110
When I would apply the 10 obs / parameter rule, it would be impossible to prevent overfitting (110 params * 10 obs = 1100 obs would be required).
I had to ask this, because I already know that this will wander my mind and steal my sleep.