Recent Developments in Machine Learning and Data Analytics IC3 2018 /

This book presents high-quality papers from an international forum for research on computational approaches to learning. It includes current research and findings from various research labs, universities and institutions that may lead to development of marketable products. It also provides solid sup...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Kalita, Jugal (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Balas, Valentina Emilia (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Borah, Samarjeet (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Pradhan, Ratika (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Singapore : Springer Singapore : Imprint: Springer, 2019.
Έκδοση:1st ed. 2019.
Σειρά:Advances in Intelligent Systems and Computing, 740
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Περιγραφή
Περίληψη:This book presents high-quality papers from an international forum for research on computational approaches to learning. It includes current research and findings from various research labs, universities and institutions that may lead to development of marketable products. It also provides solid support for these findings in the form of empirical studies, theoretical analysis, or comparison to psychological phenomena. Further, it features work that shows how to apply learning methods to solve important application problems as well as how machine learning research is conducted. The book is divided into two main parts: Machine Learning Techniques, which covers machine learning-related research and findings; and, Data Analytics, which introduces recent developments in this domain. Additionally, the book includes work on data analytics using machine learning techniques. .
Φυσική περιγραφή:XIV, 530 p. 233 illus. online resource.
ISBN:9789811312809
ISSN:2194-5357 ;
DOI:10.1007/978-981-13-1280-9