# Tag Info

7

There does not appear to be an historicial consensus on this. The Wikipedia page on the Perceptrons book (which does not come down on either side) gives an argument that the ability of MLPs to compute any Boolean function was widely known at the time (at the very least to McCulloch and Pitts). However, this page gives an account by someone present at the ...

4

Inherently, no. The MLP is just a data structure. It represents a function, but a standard MLP is just representing an input-output mapping, and there's no recursive structure to it. On the other hand, possibly your source is referring to the common algorithms that operate over MLPs, specifically forward propagation for prediction and back propagation for ...

4

It can be done. The activation function of a neuron does not have to be monotonic. The activation that Rahul suggested can be implemented via a continuously differentiable function, for example $f(s) = exp(-k(1-s)^2)$ which has a nice derivative $f'(s) = 2k~(1-s)f(s)$. Here, $s=w_0~x_0+w_1~x_1$. Therefore, standard gradient-based learning algorithms are ...

4

I assume you're talking about a perceptron threshold function. One definition of it with an explicit threshold is $$f(\textbf{x})= \begin{cases} 1& \text{if } \textbf{w}\cdot\textbf{x} > t\\ 0& \text{otherwise} \end{cases}.$$ Another form with a bias is $$f(\textbf{x})= \begin{cases} 1& \text{if } \textbf{w}\cdot\textbf{x} + b > 0\\ 0&... 3 \max(-y_i(w x_i), 0) is not partial derivable respect w if w x_i=0. Loss functions are problematic when not derivable in some point, but even more when they are flat (constant) in some interval of the weights. Assume y_i = 1 and w x_i < 0 (that is, an error of type "false negative"). In this case, function [y_i - \text{sign}(w x_i)]^2 ... 3 Sure, you can define plenty of things we don't generally need to regard as recursive as so. An MLP is just a series of functions applied to its input. This can be loosely formulated as$$ o_n = f(o_{n-1}) Where $o_n$ is the output of layer $n$. But this clearly doesn't reveal, much does it?

2

The main problems are that your activation function is not monotonic (as pointed out by csrev), and that it is not continuously differentiable. These make it very difficult / impossible to use standard gradient-based learning algorithms. So yes, there may exist a good solution of weight values, but it is very difficult to find or approximate those weight ...

2

Indeed I think the problem is with the way you've defined the activation function. By selecting it arbitrarily, you could solve many specific problems. In practice, activation functions used are monotonic. It keeps the error function convex at a per-layer level. In theory though I'm not sure exactly what Rosenblatt has claimed so it might be worth calling ...

2

In Brief: re-train your dataset. I believe where you get lower accuracy scores, your model has not converged to the final state. duplicate your dataset multiple times and create a bigger one, then train your model with it. In Detail: number_of_samples_classified_correctly/total_number_of_samples(I'm not sure this should be the correct definition for ...

2

I think your confusion comes from the fact that you are calling those two hidden nodes "perceptrons". You shouldn't call the hidden nodes in your network perceptrons. You should call them "nodes" or "neurons", although the term "multilayer perceptron" comes exactly from that. You should think of a perceptron as something like A perceptron simply computes a ...

2

The section of the book Perceptrons: An Introduction to Computational Geometry (expanded edition, third printing, 1988) that shows the limitations of the perceptron should be 11.8 The Nonseparable Case (p. 181), where the authors write There are many reasons for studying the operation of the perceptron learning program when there is no $\mathbf{A}^*$ with ...

1

In Single Perceptron / Multi-layer Perceptron(MLP), we only have linear separability because they are composed of input and output layers(some hidden layers in MLP) This is wrong. A multi-layer perceptron (i.e. a feed-forward neural network) with non-linear activation functions can perform non-linear classification and regression. In fact, an MLP with one ...

1

This is one of the main skills that separates someone with a deep understanding of, and experience in, machine learning learning, from a neophyte. There are several approaches: Try several methods, perhaps with automated hyperparameter optimization, and see if there's a big difference in typical model quality. This is pretty common if you don't have a lot ...

1

It seems I've solved the issue. There was several mistakes: 1. I've generated random weights from 0 to 1. As a result, too big numbers passed through softmax function (>10000), and the function wasn't calculated correctly. I divided each initial weight on the number of neurons in previous layer and solved the issue. 2. I've calculated separate delta for ...

1

This is a very dicey question. Logic functions can be thought of as mapping multiple inputs to a single output. Now each logic function create its own boundary. So if you are using a complex logical equation it is actually very hard to approximate the underlying function. Here I am treating the input Booleans as the input features. From practical experience:...

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Y is the desired output of the perceptron (often referred to as target) , for the given set of input vectors. Rationale behind Y.a<=0 : Prerequisite knowledge : A=A-B : Moves vector A away from direction of vector B A=A+B : Moves A in the direction of B A (.) B >0 ; A vector is directed acutely (<90 deg.) towards B vector A (.) B <0 ; A vector is ...

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