Alternating Decision Tree: Fundamentals and Applications
ამ ელწიგნის შესახებ
A categorization strategy that may be learned by machine learning is known as an alternating decision tree, or ADTree. It is connected to boosting and generalizes decision trees at the same time.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Alternating Decision Tree
Chapter 2: Decision Tree Learning
Chapter 3: AdaBoost
Chapter 4: Random Forest
Chapter 5: Gradient Boosting
Chapter 6: Propositional Calculus
Chapter 7: Support Vector Machine
Chapter 8: Method of Analytic Tableaux
Chapter 9: Boolean Satisfiability Algorithm Heuristics
Chapter 10: Multiplicative Weight Update Method
(II) Answering the public top questions about alternating decision tree.
(III) Real world examples for the usage of alternating decision tree in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of alternating decision tree' 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 alternating decision tree.
