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 = 27th I...

ILP (Conference) (2017 :)

FindBook

Google Book

Amazon

博客來

Inductive logic programming = 27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017 : revised selected papers /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Inductive logic programming/ edited by Nicolas Lachiche, Christel Vrain.
其他題名:	27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017 : revised selected papers /
其他題名:	ILP 2017
其他作者:	Lachiche, Nicolas.
團體作者:	ILP (Conference)
出版者:	Cham :Springer International Publishing : : 2018.,
面頁冊數:	x, 185 p. :ill., digital ;24 cm.
內容註:	Relational Affordance Learning for Task-dependent Robot Grasping -- Positive and Unlabeled Relational Classification Through Label Frequency Estimation -- On Applying Probabilistic Logic Programming to Breast Cancer Data -- Logical Vision: One-Shot Meta-Interpretive Learning from Real Images -- Demystifying Relational Latent Representations -- Parallel Online Learning of Event Definitions -- Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach -- Parallel Inductive Logic Programming System for Super-linear Speedup -- Inductive Learning from State Transitions over Continuous Domains -- Stacked Structure Learning for Lifted Relational Neural Networks -- Pruning Hypothesis Spaces Using Learned Domain Theories -- An Investigation into the Role of Domain-knowledge on the Use of Embeddings.
Contained By:	Springer eBooks
標題:	Logic programming - Congresses. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-78090-0
ISBN:	9783319780900

Inductive logic programming = 27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017 : revised selected papers /
Inductive logic programming27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017 : revised selected papers /[electronic resource] :ILP 2017edited by Nicolas Lachiche, Christel Vrain. - Cham :Springer International Publishing :2018. - x, 185 p. :ill., digital ;24 cm. - Lecture notes in computer science,107590302-9743 ;. - Lecture notes in computer science ;10759..

Relational Affordance Learning for Task-dependent Robot Grasping -- Positive and Unlabeled Relational Classification Through Label Frequency Estimation -- On Applying Probabilistic Logic Programming to Breast Cancer Data -- Logical Vision: One-Shot Meta-Interpretive Learning from Real Images -- Demystifying Relational Latent Representations -- Parallel Online Learning of Event Definitions -- Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach -- Parallel Inductive Logic Programming System for Super-linear Speedup -- Inductive Learning from State Transitions over Continuous Domains -- Stacked Structure Learning for Lifted Relational Neural Networks -- Pruning Hypothesis Spaces Using Learned Domain Theories -- An Investigation into the Role of Domain-knowledge on the Use of Embeddings.

This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orleans, France, in September 2017. The 12 full 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: 9783319780900

Standard No.: 10.1007/978-3-319-78090-0doiSubjects--Topical Terms:

840448
Logic programming
--Congresses.

LC Class. No.: QA76.63 / .I47 2017

Dewey Class. No.: 005.115

Inductive logic programming = 27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017 : revised selected papers /
LDR:02701nmm a2200349 a 4500 001 2136583
003 DE-He213
005 20180314160019.0
006 m d
007 cr nn 008maaau
008 181117s2018 gw s 0 eng d
020 $a 9783319780900 $q (electronic bk.)
020 $a 9783319780894 $q (paper)
024 7 $a 10.1007/978-3-319-78090-0 $2 doi
035 $a 978-3-319-78090-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.63 $b .I47 2017
072 7 $a UYA $2 bicssc
072 7 $a MAT018000 $2 bisacsh
072 7 $a COM051010 $2 bisacsh
082 0 4 $a 005.115 $2 23
090 $a QA76.63 $b .I29 2017
111 2 $a ILP (Conference) $n (27th : $d 2017 : $c Orleans, France) $3 3308094
245 1 0 $a Inductive logic programming $h [electronic resource] : $b 27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017 : revised selected papers / $c edited by Nicolas Lachiche, Christel Vrain.
246 3 $a ILP 2017
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a x, 185 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 10759
505 0 $a Relational Affordance Learning for Task-dependent Robot Grasping -- Positive and Unlabeled Relational Classification Through Label Frequency Estimation -- On Applying Probabilistic Logic Programming to Breast Cancer Data -- Logical Vision: One-Shot Meta-Interpretive Learning from Real Images -- Demystifying Relational Latent Representations -- Parallel Online Learning of Event Definitions -- Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach -- Parallel Inductive Logic Programming System for Super-linear Speedup -- Inductive Learning from State Transitions over Continuous Domains -- Stacked Structure Learning for Lifted Relational Neural Networks -- Pruning Hypothesis Spaces Using Learned Domain Theories -- An Investigation into the Role of Domain-knowledge on the Use of Embeddings.
520 $a This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orleans, France, in September 2017. The 12 full 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 Computer Science. $3 626642
650 2 4 $a Mathematical Logic and Formal Languages. $3 892517
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Programming Languages, Compilers, Interpreters. $3 891123
650 2 4 $a Logics and Meanings of Programs. $3 892495
650 2 4 $a Programming Techniques. $3 892496
700 1 $a Lachiche, Nicolas. $3 3308095
700 1 $a Vrain, Christel. $3 3308096
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Lecture notes in computer science ; $v 10759. $3 3308097
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-78090-0
950 $a Computer Science (Springer-11645)