Entropy Randomization in Machine Learning presents a new approach to machine learning - entropy randomization - to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study).
For graduate students, practitioners, and sophisticated users, this book offers a tutorial approach to the foundations of random matrix theory for machine... Læs mere
Written in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to core topics in... Læs mere
The development of a theoretical foundation for deep learning methods constitutes one of the most active and exciting research topics in applied mathematics.... Læs mere
Designed with engineers in mind, this self-contained book will equip students with everything they need to apply machine learning principles to real-world engineering... Læs mere
The idea for this book came from the time the authors spent at the Statistics and Applied Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina starting in fall 2003.
The purpose of this book is to provide an account of survival analysis. The authors intend to accomplish it from two fronts: (i) methods in survival analysis developed over the past... Læs mere
Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable.
This book explores the application use of machine learning and artificial intelligence in smart agriculture by Identifying and describing... Læs mere
This book provides a deep insight into the recent techniques which form the backbone of smart environment... Læs mere