Understanding Nonlinear Decision Boundaries in Machine Learning: A Beginner Guide
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Understanding Nonlinear Decision Boundaries in Machine Learning: A Beginner Guide

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Recall from Chapter 8, The Perceptron, that while some Boolean functions such

as AND, OR, and NAND can be approximated by the perceptron, the linearly

inseparable function XOR cannot, as shown in the following plots:

Let's review XOR in more detail to develop an intuition for the power of artificial

neural networks. In contrast to AND, which outputs 1 when both of its inputs are

equal to 1, and OR, which outputs 1 when at least one of the inputs are equal to 1,

the output of XOR is 1 when exactly one of its inputs are equal to 1. We could view

XOR as outputting 1 when two conditions are true. The first condition is that at

least one of the inputs must be equal to 1; this is the same condition that OR tests.

The second condition is that not both of the inputs are equal to 1; NAND tests this

condition. We can produce the same output as XOR by processing the input with

both OR and NAND and then verifying that the outputs of both functions are equal

to 1 using AND. That is, the functions OR, NAND, and AND can be composed to

produce the same output as XOR.

The following tables provide the truth tables for XOR, OR, AND, and NAND for the

inputs A and B. From these tables we can verify that inputting the output of OR and

NAND to AND produces the same output as inputting A and B to XOR:

A

B

A AND B

A NAND B

A OR B

A XOR B

0

0

0

1

0

0

0

1

0

1

1

1

1

0

0

1

1

1

1

1

1

0

1

0

A

B

A OR B

A NAND B

(A OR B) AND (A NAND B)

0

0

0

1

0

0

1

1

1

1

1

0

1

1

1

1

1

1

0

0

Instead of trying to represent XOR with a single perceptron, we will build an artificial

neural network from multiple artificial neurons that each approximate a linear

function. Each instance's feature representation will be input to two neurons; one

neuron will represent NAND and the other will represent OR. The output of these

neurons will be received by a third neuron that represents AND to test whether both

of XOR's conditions are true.

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