東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Recent trends in learning from data ...

INNS Big Data and Deep Learning Conference ((2019 :)

Linked to FindBook

Google Book

Amazon

博客來

Recent trends in learning from data = tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019) /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Recent trends in learning from data/ edited by Luca Oneto ... [et al.].
Reminder of title:	tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019) /
remainder title:	INNSBDDL 2019
other author:	Oneto, Luca.
corporate name:	INNS Big Data and Deep Learning Conference
Published:	Cham :Springer International Publishing : : 2020.,
Description:	vii, 221 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction: Recent Trends in Learning From Data -- Learned data structures -- Deep Randomized Neural Networks -- Tensor Decompositions and Practical Applications -- Deep learning for graphs -- Limitations of Shallow Networks -- Fairness in Machine Learning -- Online Continual Learning on Sequences.
Contained By:	Springer eBooks
Subject:	Big data - Congresses. -
Online resource:	https://doi.org/10.1007/978-3-030-43883-8
ISBN:	9783030438838

Recent trends in learning from data = tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019) /
Recent trends in learning from datatutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019) /[electronic resource] :INNSBDDL 2019edited by Luca Oneto ... [et al.]. - Cham :Springer International Publishing :2020. - vii, 221 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.8961860-949X ;. - Studies in computational intelligence ;v.896..

Introduction: Recent Trends in Learning From Data -- Learned data structures -- Deep Randomized Neural Networks -- Tensor Decompositions and Practical Applications -- Deep learning for graphs -- Limitations of Shallow Networks -- Fairness in Machine Learning -- Online Continual Learning on Sequences.

This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research.

ISBN: 9783030438838

Standard No.: 10.1007/978-3-030-43883-8doiSubjects--Topical Terms:

3166510
Big data
--Congresses.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Recent trends in learning from data = tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019) /
LDR:02061nmm a2200349 a 4500 001 2217151
003 DE-He213
005 20200805092442.0
006 m d
007 cr nn 008maaau
008 201120s2020 sz s 0 eng d
020 $a 9783030438838 $q (electronic bk.)
020 $a 9783030438821 $q (paper)
024 7 $a 10.1007/978-3-030-43883-8 $2 doi
035 $a 978-3-030-43883-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.B45
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 005.7 $2 23
090 $a QA76.9.B45 $b I58 2019
111 2 $a INNS Big Data and Deep Learning Conference $d (2019 : $c Sestri Levante, Italy) $3 3450153
245 1 0 $a Recent trends in learning from data $h [electronic resource] : $b tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019) / $c edited by Luca Oneto ... [et al.].
246 3 $a INNSBDDL 2019
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a vii, 221 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.896
505 0 $a Introduction: Recent Trends in Learning From Data -- Learned data structures -- Deep Randomized Neural Networks -- Tensor Decompositions and Practical Applications -- Deep learning for graphs -- Limitations of Shallow Networks -- Fairness in Machine Learning -- Online Continual Learning on Sequences.
520 $a This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research.
650 0 $a Big data $v Congresses. $3 3166510
650 0 $a Machine learning $x Congresses. $3 576368
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Data Engineering. $3 3409361
700 1 $a Oneto, Luca. $3 3442884
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Studies in computational intelligence ; $v v.896. $3 3450154
856 4 0 $u https://doi.org/10.1007/978-3-030-43883-8
950 $a Intelligent Technologies and Robotics (Springer-42732)