6 ms·

No, that's a pretty standard trivial example for NNs. http://en.wikipedia.org/wiki/Feedforward_neural_network#Multi-layer_perceptron http://en.wikipedia.org/wi

No, that's a pretty standard trivial example for NNs. http://en.wikipedia.org/wiki/Feedforward_neural_network#Multi-layer_perceptron http://en.wikipedia.org/wiki/Feedforward_neural_network#Mult...

abrichr 12y ago
The reason why it's used is because it's a very simple example of a non-linearly separable function. You can interpret boolean functions as classification problems: given some input(s) x, classify the output as either 1 (class A) or 0 (class B). The truth table of an XOR function looks like this: x1 | x2 | XOR | Class --------------------- 0 | 0 | 0 | B 0 | 1 | 1 | A 1 | 0 | 1 | A 1 | 1 | 0 | B If you were to plot the XOR function in R2, it would look something like this: x2 ^ | | A B | | B-----A-----> x1 To be non-linearly separable means that there is no straight line that you can draw in the above plot that will split the classes. Thus, we need a non-linear classifier, e.g. a neural net.
- klapinat0r 12y ago
  Would you mind elaborating the linear separable part? As in, the following would be linear separable (all labels on the same side of the line)? x2 ^ | | A A | | B-----B-----> x1
  - cyorir 12y ago
    It's a bit hard to elaborate. You have to rember that a classification problem may be generalized to more than just two dimensions, in fact it can be generalized to however many dimensions are necessary to describe the data. In the general multidimensional cases linearly separable relates to the number and type of hyperplanes (subspaces) necessary to correctly classify the data. In this simple 2-d case the subspace is a line, so this example is linearly separable. In the 3rd dimensional case linearly separable refers to classification using a single 2-dimensional plane. For 4 dimensions with the data, you need a single 3-dimensional hyperplane for it to be linearly separable, and so on.
  - dnautics 12y ago
    generally a linear function takes the form of: v(x1,x2) = C1 * x1 + C2 * x2 + C3. C's being "coefficients". In your case, v(x1,x2) = 0x1 + x2 + 0 i.e. C1 = 0; C2 = 1; C3 = 0 suffices, where a result v > 0.5 indicates A and v <= 0.5 indicates B.
darkxanthos 12y ago
Interesting. Thanks for that

No, that's a pretty standard trivial example for NNs. http://en.wikipedia.org/wiki/Feedforward_neural_network#Multi-layer_perceptron http://en.wikipedia.org/wiki/Feedforward_neural_network#Mult...

abrichr 12y ago

The reason why it's used is because it's a very simple example of a non-linearly separable function. You can interpret boolean functions as classification problems: given some input(s) x, classify the output as either 1 (class A) or 0 (class B). The truth table of an XOR function looks like this: x1 | x2 | XOR | Class --------------------- 0 | 0 | 0 | B 0 | 1 | 1 | A 1 | 0 | 1 | A 1 | 1 | 0 | B If you were to plot the XOR function in R2, it would look something like this: x2 ^ | | A B | | B-----A-----> x1 To be non-linearly separable means that there is no straight line that you can draw in the above plot that will split the classes. Thus, we need a non-linear classifier, e.g. a neural net.

klapinat0r 12y ago
Would you mind elaborating the linear separable part? As in, the following would be linear separable (all labels on the same side of the line)? x2 ^ | | A A | | B-----B-----> x1
- cyorir 12y ago
  It's a bit hard to elaborate. You have to rember that a classification problem may be generalized to more than just two dimensions, in fact it can be generalized to however many dimensions are necessary to describe the data. In the general multidimensional cases linearly separable relates to the number and type of hyperplanes (subspaces) necessary to correctly classify the data. In this simple 2-d case the subspace is a line, so this example is linearly separable. In the 3rd dimensional case linearly separable refers to classification using a single 2-dimensional plane. For 4 dimensions with the data, you need a single 3-dimensional hyperplane for it to be linearly separable, and so on.
- dnautics 12y ago
  generally a linear function takes the form of: v(x1,x2) = C1 * x1 + C2 * x2 + C3. C's being "coefficients". In your case, v(x1,x2) = 0x1 + x2 + 0 i.e. C1 = 0; C2 = 1; C3 = 0 suffices, where a result v > 0.5 indicates A and v <= 0.5 indicates B.

darkxanthos 12y ago

Interesting. Thanks for that