Naive Bayes Classifier: Fundamentals and Applications
この電子書籍について
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.
