Naive Bayes Classifier - Fundamentals and Applications
Fouad Sabry
Maison d'édition: One Billion Knowledgeable
Synopsis
What Is Naive Bayes Classifier In the field of statistics, naive Bayes classifiers are a family of straightforward "probabilistic classifiers" that are derived from the application of Bayes' theorem with strong (naive) assumptions of independence between the features. They are among the Bayesian network models that are the simplest, but when combined with kernel density estimation, they are capable of achieving great levels of accuracy. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Naive Bayes classifier Chapter 2: Likelihood function Chapter 3: Bayes' theorem Chapter 4: Bayesian inference Chapter 5: Multivariate normal distribution Chapter 6: Maximum likelihood estimation Chapter 7: Bayesian network Chapter 8: Naive Bayes spam filtering Chapter 9: Marginal likelihood Chapter 10: Dirichlet distribution (II) Answering the public top questions about naive bayes classifier. (III) Real world examples for the usage of naive bayes classifier in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of naive bayes classifier' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of naive bayes classifier.
Disponible depuis: 23/06/2023.
Longueur d'impression: 198 pages.
