Spectral Feature Selection for Data Mining (Open Access)
Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framework for supervised, unsupervised, and semisupervise
CHF 123.85
Preise inkl. MwSt. und Versandkosten (Portofrei ab CHF 40.00)
Versandkostenfrei
Produktdetails
- ISBN: 978-1-4398-6210-0
- EAN: 9781439862100
- Produktnummer: 13955164
- Verlag: Crc Press
- Sprache: Englisch
- Erscheinungsjahr: 2011
- Seitenangabe: 224 S.
- Plattform: PDF
- Masse: 0 KB
Über den Autor
Zheng Zhao is a research statistician at the SAS Institute, Inc. His recent research focuses on designing and developing novel analytic approaches for handling large-scale data of extremely high dimensionality. Dr. Zhao is the author of PROC HPREDUCE, which is a SAS High Performance Analytics procedure for large-scale parallel variable selection. He was co-chair of the 2010 PAKDD Workshop on Feature Selection in Data Mining. He earned a Ph.D. in computer science and engineering from Arizona State University. Huan Liu is a professor of computer science and engineering at Arizona State University. Dr. Liu serves on journal editorial boards and conference program committees and is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction. He earned a Ph.D. in computer science from the University of Southern California. With a focus on data mining, machine learning, social computing, and artificial intelligence, his research investigates problems in real-world application with high-dimensional data of disparate forms, such as social media, group interaction and modeling, data preprocessing, and text/web mining.
45 weitere Werke von Huan Liu:
Combating Online Hostile Posts in Regional Languages during Emergency Situation
Ebook (PDF Format)
CHF 82.50
Bewertungen
Anmelden