Beschreibung

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.

Summarizes the latest applications of robust optimization in data mining

An essential accompaniment for theoreticians and data miners

Includes supplementary material: sn.pub/extras

Inhalt
1. Introduction.- 2. Least Squares Problems.- 3. Principal Component Analysis.- 4. Linear Discriminant Analysis.- 5. Support Vector Machines.- 6. Conclusion.

Produktinformationen

Titel:	Robust Data Mining
Autor:	Petros Xanthopoulos   Theodore B. Trafalis   Panos M. Pardalos
EAN:	9781441998774
ISBN:	1441998772
Format:	Kartonierter Einband
Herausgeber:	Springer New York
Anzahl Seiten:	72
Gewicht:	125g
Größe:	H235mm x B155mm x T4mm
Jahr:	2012
Untertitel:	Englisch
Auflage:	2013