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MATLAB for Machine Learning

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

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781788398435
Length 382 pages
Edition 1st Edition
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Authors (2):
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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
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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...

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