Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise.This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems. This brief will appeal to theoreticians and data miners working in this field.
Rezensionen / Stimmen
From the reviews:
"The goal of the book is to provide a guide for junior researchers interested in pursuing theoretical research in data mining and robust optimization and has been developed so that each chapter can be studied independent of the others." (Hans Benker, Zentralblatt MATH, Vol. 1260, 2013)
Reihe
Sprache
Verlagsort
Verlagsgruppe
Illustrationen
6
6 s/w Abbildungen
XII, 59 p. 6 illus.
Dateigröße
ISBN-13
978-1-4419-9878-1 (9781441998781)
DOI
10.1007/978-1-4419-9878-1
Schweitzer Klassifikation
1. Introduction.- 2. Least Squares Problems.- 3. Principal Component Analysis.- 4. Linear Discriminant Analysis.- 5. Support Vector Machines.- 6. Conclusion.
