Statistics and Machine Learning Techniques
Regression Model and Radial Basis Functions Model
(Sprache: Englisch)
This book explains the comparison of Statistical Technique and Machine Learning Technique, specifically the Regression Model and the Radial Basis Function Neural Network (RBFNN) Model. This explanation involves the Mathematical theory and principle upon...
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This book explains the comparison of Statistical Technique and Machine Learning Technique, specifically the Regression Model and the Radial Basis Function Neural Network (RBFNN) Model. This explanation involves the Mathematical theory and principle upon which RBFNN model was developed and it manages the generally belief that Machine Learning techniques are "Black Box" meaning that the Mathematics of Neural Network cannot be explained. Therefore, this book explains the Mathematics of the Radial Basis Functions which depends on the Gaussian function. Some estimates of the two models were compared and explained in this book such as the Sum of Squares of Errors, Bayesian Information Criterion and the Relative Importance of each explanatory variable.
Autoren-Porträt von Daniel Akinboro
Akinboro, DanielMr Akinboro is a Statistician, a Data Analyst, a Programmer, a Business Evaluator and a Lecturer of Advance Mathematics, Statistics and Physics with a six years experience. The author holds a B.Sc. (Hons) in Statistics, an M.Sc. (Hons) in Statistics and currently is pursuing a Masters in Business Administration.
Bibliographische Angaben
- Autor: Daniel Akinboro
- 2021, 64 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 6203583960
- ISBN-13: 9786203583960
Sprache:
Englisch
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