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Statistical Classification - Fundamentals and Applications - cover

Statistical Classification - Fundamentals and Applications

Fouad Sabry

Maison d'édition: One Billion Knowledgeable

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Synopsis

What Is Statistical Classification
 
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.
Disponible depuis: 23/06/2023.
Longueur d'impression: 129 pages.

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