Functions Vs Probability Distribution Functions (PDF):
Is the neural network learning a function or a pdf?
PDF:
PDF is also a function but with certain restrictions and rules.
Example: The input of PDF is restricted - possible inputs can only be taken from a sample space ( containing possible values of Random Variable (RV))
Input is finite [bounded] so the sum of output of those finite input is 1.
[https://stats.stackexchange.com/questions/347431]
Strictly speaking, neural networks are fitting a non-linear function.
They can be interpreted as fitting a probability density function if suitable activation functions are chosen and certain conditions are respected (Values must be positive and ≤ 1, etc...). But that is a question of how you choose to interpret their output, not of what they are actually doing. Under the hood, they are still non-linear function estimators, which you are choosing to apply to the specific problem of PDF estimation.
Classifier is basically a thresholded regressor.
Is the neural network learning a function or a pdf?
PDF:
PDF is also a function but with certain restrictions and rules.
Example: The input of PDF is restricted - possible inputs can only be taken from a sample space ( containing possible values of Random Variable (RV))
Input is finite [bounded] so the sum of output of those finite input is 1.
[https://stats.stackexchange.com/questions/347431]
Strictly speaking, neural networks are fitting a non-linear function.
They can be interpreted as fitting a probability density function if suitable activation functions are chosen and certain conditions are respected (Values must be positive and ≤ 1, etc...). But that is a question of how you choose to interpret their output, not of what they are actually doing. Under the hood, they are still non-linear function estimators, which you are choosing to apply to the specific problem of PDF estimation.
Classifier is basically a thresholded regressor.