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Machine Learning Algorithms

You're reading from Machine Learning Algorithms A reference guide to popular algorithms for data science and machine learning

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781785889622

Length 360 pages

Edition 1st Edition

Languages

Python

Tools

Processing

Concepts

Data Science

Author (1):

Giuseppe Bonaccorso

View More author details

Table of Contents (16) Chapters

Preface

1. A Gentle Introduction to Machine Learning FREE CHAPTER

2. Important Elements in Machine Learning

3. Feature Selection and Feature Engineering

4. Linear Regression

5. Logistic Regression

6. Naive Bayes

7. Support Vector Machines

8. Decision Trees and Ensemble Learning

9. Clustering Fundamentals

10. Hierarchical Clustering

11. Introduction to Recommendation Systems

12. Introduction to Natural Language Processing

13. Topic Modeling and Sentiment Analysis in NLP

14. A Brief Introduction to Deep Learning and TensorFlow

15. Creating a Machine Learning Architecture

Linear support vector machines

Let's consider a dataset of feature vectors we want to classify:

For simplicity, we assume it as a binary classification (in all the other cases, it's possible to use automatically the one-versus-all strategy) and we set our class labels as -1 and 1:

Our goal is to find the best separating hyperplane, for which the equation is:

In the following figure, there's a bidimensional representation of such a hyperplane:

In this way, our classifier can be written as:

In a realistic scenario, the two classes are normally separated by a margin with two boundaries where a few elements lie. Those elements are called support vectors. For a more generic mathematical expression, it's preferable to renormalize our dataset so that the support vectors will lie on two hyperplanes with equations:

In the following figure, there's an example with two support vectors. The dashed line is the original separating hyperplane:

Our goal is to maximize the distance between these two boundary hyperplanes...

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