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

Format:

Book

Author/Creator:

Xanthopoulos, Petros

Contributor:

Pardalos, P. M. (Panos M.), 1954-

Trafalis, Theodore B., 1959-

Series:

SpringerBriefs in optimization

Language:

English

Subjects (All):

Data mining.

Robust optimization.

Data Mining.

Medical Subjects:

Data Mining.

Physical Description:

1 online resource

Place of Publication:

New York Springer ©2013

Language Note:

English

System Details:

text file

PDF

Summary:

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

Contents:

Least squares problems Principal component analysis Linear discriminant analysis Support vector machines Conclusion

Notes:

Includes bibliographical references

Print version record

Other Format:

Print version Xanthopoulos, Petros. Robust data mining

ISBN:

9781441998781

1441998780

1441998772

9781441998774

9781283909174

1283909170

OCLC:

819571506

Access Restriction:

Restricted for use by site license