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Machine Learning with Swift

You're reading from   Machine Learning with Swift Artificial Intelligence for iOS

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781787121515
Length 378 pages
Edition 1st Edition
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Authors (3):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Oleksandr Baiev Oleksandr Baiev
Author Profile Icon Oleksandr Baiev
Oleksandr Baiev
Alexander Sosnovshchenko Alexander Sosnovshchenko
Author Profile Icon Alexander Sosnovshchenko
Alexander Sosnovshchenko
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Table of Contents (14) Chapters Close

Preface 1. Getting Started with Machine Learning FREE CHAPTER 2. Classification – Decision Tree Learning 3. K-Nearest Neighbors Classifier 4. K-Means Clustering 5. Association Rule Learning 6. Linear Regression and Gradient Descent 7. Linear Classifier and Logistic Regression 8. Neural Networks 9. Convolutional Neural Networks 10. Natural Language Processing 11. Machine Learning Libraries 12. Optimizing Neural Networks for Mobile Devices 13. Best Practices

Calculating the distance


How do we calculate a distance? Well, that depends on the kind of problem. In two-dimensional space, we used to calculate the distance between two points, (x1, y1) and (x2, y2), as 

—the Euclidean distance. But this is not how taxi drivers calculate distance because in the city you can't cut corners and go straight to your goal. So, they use (knowing it or not) another distance metric: Manhattan distance or taxicab distance, also known as l1-norm:

. This is the distance if we're only allowed to move along coordinate axes:

Figure 3.1: The blue line represents the Euclidean distance, the red line represents the Manhattan distance. Map of Manhattan by OpenStreetMap

Jewish German mathematician Hermann Minkowski proposed a generalization of both Euclidean and Manhattan distances. Here is the formula for the Minkowski distance:

where p and q are n-dimensional vectors (or coordinates of points in n-dimensional space if you wish). But what does c stand for? It is an order of...

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