Stream Data Mining: Algorithms and Their Probabilistic Properties / Studies in Big Data Bd.56 (PDF)
This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how to...
- Kreditkarte, Paypal, Rechnung
- Kostenloser tolino webreader
This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who dealwith stream data, e.g. in telecommunication, banking, and sensor networks.
- Autoren: Leszek Rutkowski , Maciej Jaworski , Piotr Duda
- 2019, 1st ed. 2020, 330 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 303013962X
- ISBN-13: 9783030139629
- Erscheinungsdatum: 16.03.2019
Abhängig von Bildschirmgrösse und eingestellter Schriftgrösse kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Grösse: 10 MB
- Ohne Kopierschutz
- Vorlesefunktion
| Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
|---|
Schreiben Sie einen Kommentar zu "Stream Data Mining: Algorithms and Their Probabilistic Properties / Studies in Big Data Bd.56".
Kommentar verfassen