Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	Januar 2019
Genre:	EDV / Informatik
	Application software / Artificial Intelligence / C / Computer Appl. in Social and Behavioral Sciences / Computer Application in Social and Behavioral Sciences / Computer applications in the social & behavioural sciences / computer science / Computer security / Computer Vision / Computers / Computersicherheit / Computing Milieux / Data and Information Security / Data Mining / Data Mining and Knowledge Discovery / data protection / Expert systems / knowledge-based systems / Image processing / Image Processing and Computer Vision / Information technology# general issues / Informationstechnik (IT), allgemeine Themen / Maschinelles Sehen, Bildverstehen / Netzwerksicherheit / Optical data processing / Security / Wissensbasierte Systeme, Expertensysteme mehr...
ISBN:	9783030109240
EAN-Code:	9783030109240
Verlag:	Springer EN
Einband:	Kartoniert
Sprache:	English
Serie:	Lecture Notes in Artificial Intelligence #11051 - Lecture Notes in Computer Science
Dimensionen:	H 235 mm / B 155 mm / D 41 mm
Gewicht:	1175 gr
Seiten:	740
Illustration:	XXXVIII, 740 p. 451 illus., 159 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

The three volume proceedings LNAI 11051 - 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learningensemble methods; and evaluation. Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.