Stream Data Mining: Algorithms and Their Probabilistic Properties 1st ed. 2020 [Kõva köide]

  • Formaat: Hardback, 330 pages, kõrgus x laius: 235x155 mm, kaal: 672 g, 63 Illustrations, color; 48 Illustrations, black and white; IX, 330 p. 111 illus., 63 illus. in color., 1 Hardback
  • Sari: Studies in Big Data 56
  • Ilmumisaeg: 26-Mar-2019
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030139611
  • ISBN-13: 9783030139612
  • Kõva köide
  • Hind: 115,79 EUR*
  • Tavahind: 154,39 EUR
  • Säästad 25%
  • Lisa soovinimekirja
  • Lisa ostukorvi
  • Kogus:
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Raamatut on võimalik tellida. Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat.
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Formaat: Hardback, 330 pages, kõrgus x laius: 235x155 mm, kaal: 672 g, 63 Illustrations, color; 48 Illustrations, black and white; IX, 330 p. 111 illus., 63 illus. in color., 1 Hardback
  • Sari: Studies in Big Data 56
  • Ilmumisaeg: 26-Mar-2019
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030139611
  • ISBN-13: 9783030139612

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 deal with stream data, e.g. in telecommunication, banking, and sensor networks.

Introduction and Overview of the Main Results of the Book.- Basic concepts of data stream mining.- Decision Trees in Data Stream Mining.- Splitting Criteria based on the McDiarmid's Theorem.

Tellige see raamat tutvumiseks meie kauplusesse!Raekoja plats 11, 51004 Tartu

Juhul, kui soovite raamatuga enne ostu tutvuda, siis palun sisestaga allpool oma nimi ning e-mail.
Võimaluse korral tellime raamatu poodi ning teavitame ka teid, kui raamat on müügile jõudnud.

* - väljad on kohustuslikud