Statistical Classification: Fundamentals and Applications
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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.
