Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
MATLAB for Machine Learning

You're reading from   MATLAB for Machine Learning Practical examples of regression, clustering and neural networks

Arrow left icon
Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781788398435
Length 382 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Pavan Kumar Kolluru Pavan Kumar Kolluru
Author Profile Icon Pavan Kumar Kolluru
Pavan Kumar Kolluru
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
Arrow right icon
View More author details
Toc

Table of Contents (10) Chapters Close

Preface 1. Getting Started with MATLAB Machine Learning FREE CHAPTER 2. Importing and Organizing Data in MATLAB 3. From Data to Knowledge Discovery 4. Finding Relationships between Variables - Regression Techniques 5. Pattern Recognition through Classification Algorithms 6. Identifying Groups of Data Using Clustering Methods 7. Simulation of Human Thinking - Artificial Neural Networks 8. Improving the Performance of the Machine Learning Model - Dimensionality Reduction 9. Machine Learning in Practice

Probabilistic classification algorithms - Naive Bayes

Bayesian classification is a statistical technique that determines the probability that an element belongs to a particular class. For example, this technique can be used to estimate the probability of a customer belonging to the class of sports car buyers, given some customer attributes such as type of work performed, age, income, civil status, sports practiced, and so on.

The technique is based on the theorem of Bayes, a mathematician and British Presbyterian minister of the eighteenth century. The theorem defines the posterior probability of an event with respect to another. Posterior, in this context, means after taking into account the events relevant to the particular case being examined as if they have already happened.

The Bayesian classifier algorithm assumes that the effect of an event on a given class is independent...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image