1.

Record Nr.

UNINA9910437873403321

Autore

Xanthopoulos Petros

Titolo

Robust data mining / / Petros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis

Pubbl/distr/stampa

New York, : Springer, 2013

ISBN

1-283-90917-0

1-4419-9878-0

Edizione

[1st ed. 2013.]

Descrizione fisica

1 online resource (66 p.)

Collana

SpringerBriefs in optimization, , 2190-8354

Altri autori (Persone)

PardalosP. M <1954-> (Panos M.)

TrafalisTheodore B

Disciplina

006.312

Soggetti

Data mining

Robust optimization

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references.

Nota di contenuto

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

Sommario/riassunto

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.