<p>The approximation theorem says you can approximate anything. But this is kind of meaningless in so far as you can do KNN and get an arbitrary approximation of your training data also.</p>

<p>Proving CNN correctly extract features is, I don't think possible. Or if it is, something involving VC theory is probably the best you can do.</p>

CC BY-SA 4.0
2020-02-15T12:15:31.427Z
The statement "The approximation theorem says you can approximate anything." is false. Which approximation are you talking about? The approximation theorems are usually about approximating continuous functions, which is far from being anything.
nbro
CC BY-SA 4.0
2020-02-16T01:09:57.893Z
@nbro OTOH we are only talking about approximation. Obviously continuous functions cannot be discontinuous, but they can approximate many discontinuous functions — probably something like any function with a finite number of discontinuities. That seems to be in agreement with [this](https://arxiv.org/pdf/1709.05289.pdf), for example.
senderle
CC BY-SA 4.0
2020-02-15T03:37:00.683Z
2020-02-15T03:37:00.683Z
FourierFlux
<p>I'm trying to really understand how multi-layer perceptrons work. I want to prove mathematically that MLP's can classify handwritten digits. The only thing I really have is that each perceptron can operate exactly like a logical operand, which obviously can classify things, and, with backpropagation and linear classification, it's obvious that, if a certain pattern exists, it'll activate the correct gates in order to classify correctly, but that is not a mathematical proof.</p>

CC BY-SA 4.0
2020-02-14T23:51:27.673Z
This is probably a special case of the universal approximation theorem. [Here](https://en.m.wikipedia.org/wiki/Universal_approximation_theorem) is a Wikipedia page about the theorem.
senderle
<classification><proofs><multilayer-perceptrons><perceptron>
Is there a mathematical theory behind why MLP can classify handwritten digits?
user8714896
# Is there a mathematical theory behind why MLP can classify handwritten digits?

(Asked by: username_0 on 2020-02-14)

I'm trying to really understand how multi-layer perceptrons work. I want to prove mathematically that MLP's can classify handwritten digits. The only thing I really have is that each perceptron can operate exactly like a logical operand, which obviously can classify things, and, with backpropagation and linear classification, it's obvious that, if a certain pattern exists, it'll activate the correct gates in order to classify correctly, but that is not a mathematical proof.

**Tags:** classification, proofs, multilayer-perceptrons, perceptron

> **Comments:**
> **username_1**: This is probably a special case of the universal approximation theorem. [Here](https://en.m.wikipedia.org/wiki/Universal_approximation_theorem) is a Wikipedia page about the theorem.  

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## 1 answers

### Answer by username_2 on 2020-02-15. Score: -1

The approximation theorem says you can approximate anything. But this is kind of meaningless in so far as you can do KNN and get an arbitrary approximation of your training data also.

Proving CNN correctly extract features is, I don't think possible. Or if it is, something involving VC theory is probably the best you can do.

> **Comments:**
> **username_3**: The statement "The approximation theorem says you can approximate anything." is false. Which approximation are you talking about? The approximation theorems are usually about approximating continuous functions, which is far from being anything.  
> **username_1**: @username_3 OTOH we are only talking about approximation. Obviously continuous functions cannot be discontinuous, but they can approximate many discontinuous functions — probably something like any function with a finite number of discontinuities. That seems to be in agreement with [this](https://arxiv.org/pdf/1709.05289.pdf), for example.  

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