Pattern Recognition in Machine Learning: Explained

• Neural networks are used in applications such as facial recognition.
• These applications utilize pattern recognition.
• This type of classification can be performed using Perceptron.
• Perceptrons can be used to classify data into two parts.
• The perceptron is also known as a linear binary classifier.

Sample classification

Explore pattern recognition in machine learning, including the key concepts, practical examples, important considerations, and useful guidance.

A perceptron can be trained to identify points lying on a straight line, without needing to know the formula for that line.

How to program neural networks

To program a neural network, we can use a simple JavaScript program to:

1. Create a simple graphing object.
2. Generate 500 random x, y points.
3. Display the x and y points.
4. Create a function to draw a line: f(x)
5. Display straight lines
6. Calculate the desired results.
7. Display the desired results

Create a simple graphing object

Creating a simple graphing object is described in AI Canvas.

For example:

const plotter = new XYPlotter("myCanvas"); plotter.transformXY(); const xMax = plotter.xMax; const yMax = plotter.yMax; const xMin = plotter.xMin; const yMin = plotter.yMin;

Generate random X and Y points

• Create as many xy points as you like.
• Let the value of x be random (from 0 to the maximum value).
• Let the value of y be random (from 0 to the maximum value).
• Display the points on the graph:

For example:

const numPoints = 500; const xPoints = []; const yPoints = []; for (let i = 0; i < numPoints; i++) { xPoints[i] = Math.random() * xMax; yPoints[i] = Math.random() * yMax; }

Create a function to draw a straight line

Displaying a line on the plotter:

For example:

function f(x) { return x * 1.2 + 50; }

Calculate the correct answer

Calculate the correct answer based on the linear function:

y = x * 1.2 + 50.

The desired answer is 1 if y lies on the line and 0 if y lies below the line.

Store the desired answers in an array (desired[]).

For example:

let desired = []; for (let i = 0; i < numPoints; i++) { desired[i] = 0; if (yPoints[i] > f(xPoints[i])) {desired[i] = 1;} }

Show the correct answers

For each point, if desired[i] = 1, display the point in black; otherwise, display the point in blue.

For example:

for (let i = 0; i < numPoints; i++) { let color = "blue"; if (desired[i]) color = "black"; plotter.plotPoint(xPoints[i], yPoints[i], color); }

How to train a Perceptron

In the next chapter, you will learn how to use the correct answers to: Train a perceptron to predict the output value of unknown input values.