Inductive Logic Programming : 27th International Conference, ILP 2017, Orléans, France, September 4-6, 2017, Revised Selected Papers

Christel Vrain

ISBN 10: 3319780891 / ISBN 13: 9783319780894

Published by Springer International Publishing, Springer International Publishing, 2018

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orléans, 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.

Seller Inventory # 9783319780894

Bibliographic Details

• Title: Inductive Logic Programming : 27th International Conference, ILP 2017, Orléans, France, September 4-6, 2017, Revised Selected Papers
• Publisher: Springer International Publishing, Springer International Publishing
• Publication Date: 2018
• Language: English
• ISBN 10: 3319780891
• ISBN 13: 9783319780894
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 11D millimeters 235 millimeters width by 155 millimeters height by 11 millimeters depth
• Weight: 306 grams

About this title

Synopsis

This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orléans, 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.