東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Inductive logic programming = 29th I...

ILP (Conference) (2019 :)

FindBook

Google Book

Amazon

博客來

Inductive logic programming = 29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3-5, 2019 : proceedings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Inductive logic programming/ edited by Dimitar Kazakov, Can Erten.
其他題名:	29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3-5, 2019 : proceedings /
其他題名:	ILP 2019
其他作者:	Kazakov, Dimitar.
團體作者:	ILP (Conference)
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	ix, 145 p. :ill., digital ;24 cm.
內容註:	CONNER: A Concurrent ILP Learner in Description Logic -- Towards Meta-interpretive Learning of Programming Language Semantics -- Towards an ILP Application in Machine Ethics -- On the Relation Between Loss Functions and T-Norms -- Rapid Restart Hill Climbing for Learning Description Logic Concepts -- Neural Networks for Relational Data -- Learning Logic Programs from Noisy State Transition Data -- A New Algorithm for Computing Least Generalization of a Set of Atoms -- LazyBum: Decision Tree Learning Using Lazy Propositionalization -- Weight Your Words: the Effect of Different Weighting Schemes on Wordification Performance -- Learning Probabilistic Logic Programs over Continuous Data.
Contained By:	Springer Nature eBook
標題:	Logic programming - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-030-49210-6
ISBN:	9783030492106

Inductive logic programming = 29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3-5, 2019 : proceedings /
Inductive logic programming29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3-5, 2019 : proceedings /[electronic resource] :ILP 2019edited by Dimitar Kazakov, Can Erten. - Cham :Springer International Publishing :2020. - ix, 145 p. :ill., digital ;24 cm. - Lecture notes in computer science,117700302-9743 ;. - Lecture notes in computer science ;11770..

CONNER: A Concurrent ILP Learner in Description Logic -- Towards Meta-interpretive Learning of Programming Language Semantics -- Towards an ILP Application in Machine Ethics -- On the Relation Between Loss Functions and T-Norms -- Rapid Restart Hill Climbing for Learning Description Logic Concepts -- Neural Networks for Relational Data -- Learning Logic Programs from Noisy State Transition Data -- A New Algorithm for Computing Least Generalization of a Set of Atoms -- LazyBum: Decision Tree Learning Using Lazy Propositionalization -- Weight Your Words: the Effect of Different Weighting Schemes on Wordification Performance -- Learning Probabilistic Logic Programs over Continuous Data.

This book constitutes the refereed conference proceedings of the 29th International Conference on Inductive Logic Programming, ILP 2019, held in Plovdiv, Bulgaria, in September 2019. The 11 papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.

ISBN: 9783030492106

Standard No.: 10.1007/978-3-030-49210-6doiSubjects--Topical Terms:

840448
Logic programming
--Congresses.

LC Class. No.: QA76.63 / .I47 2019

Dewey Class. No.: 005.115

Inductive logic programming = 29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3-5, 2019 : proceedings /
LDR:02594nmm a2200361 a 4500 001 2257955
003 DE-He213
005 20200703070814.0
006 m d
007 cr nn 008maaau
008 220420s2020 sz s 0 eng d
020 $a 9783030492106 $q (electronic bk.)
020 $a 9783030492090 $q (paper)
024 7 $a 10.1007/978-3-030-49210-6 $2 doi
035 $a 978-3-030-49210-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.63 $b .I47 2019
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 005.115 $2 23
090 $a QA76.63 $b .I29 2019
111 2 $a ILP (Conference) $n (29th : $d 2019 : $c Plovdiv, Bulgaria) $3 3529599
245 1 0 $a Inductive logic programming $h [electronic resource] : $b 29th International Conference, ILP 2019, Plovdiv, Bulgaria, September 3-5, 2019 : proceedings / $c edited by Dimitar Kazakov, Can Erten.
246 3 $a ILP 2019
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a ix, 145 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 11770
490 1 $a Lecture notes in artificial intelligence
505 0 $a CONNER: A Concurrent ILP Learner in Description Logic -- Towards Meta-interpretive Learning of Programming Language Semantics -- Towards an ILP Application in Machine Ethics -- On the Relation Between Loss Functions and T-Norms -- Rapid Restart Hill Climbing for Learning Description Logic Concepts -- Neural Networks for Relational Data -- Learning Logic Programs from Noisy State Transition Data -- A New Algorithm for Computing Least Generalization of a Set of Atoms -- LazyBum: Decision Tree Learning Using Lazy Propositionalization -- Weight Your Words: the Effect of Different Weighting Schemes on Wordification Performance -- Learning Probabilistic Logic Programs over Continuous Data.
520 $a This book constitutes the refereed conference proceedings of the 29th International Conference on Inductive Logic Programming, ILP 2019, held in Plovdiv, Bulgaria, in September 2019. The 11 papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.
650 0 $a Logic programming $v Congresses. $3 840448
650 0 $a Induction (Logic) $x Congresses. $3 654795
650 0 $a Machine learning $x Congresses. $3 576368
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Mathematical Logic and Formal Languages. $3 892517
650 2 4 $a Logics and Meanings of Programs. $3 892495
650 2 4 $a Programming Languages, Compilers, Interpreters. $3 891123
650 2 4 $a Computer Applications. $3 891249
650 2 4 $a Information Systems and Communication Service. $3 891044
700 1 $a Kazakov, Dimitar. $3 892695
700 1 $a Erten, Can. $3 3529600
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computer science ; $v 11770. $3 3529601
830 0 $a Lecture notes in artificial intelligence. $3 3382562
856 4 0 $u https://doi.org/10.1007/978-3-030-49210-6
950 $a Computer Science (SpringerNature-11645)