Statistical Classification: Fundamentals and Applications

In the field of statistics, the problem of classification refers to the task of determining which of a number of categories (sub-populations) an observation belongs to. Assigning a particular email to the "spam" or "non-spam" class is one example; another is providing a diagnosis to a patient on the basis of observed features of that patient.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Statistical classification

Chapter 2: Supervised learning

Chapter 3: Support vector machine

Chapter 4: Naive Bayes classifier

Chapter 5: Linear classifier

Chapter 6: Decision tree learning

Chapter 7: Generative model

Chapter 8: Feature (machine learning)

Chapter 9: Multinomial logistic regression

Chapter 10: Probabilistic classification

(II) Answering the public top questions about statistical classification.

(III) Real world examples for the usage of statistical classification in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of statistical classification' 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 statistical classification.