kLog: a language for logical and relational learning with kernels

DOI10.1016/j.artint.2014.08.003zbMath1405.68288arXiv1205.3981OpenAlexW2114190652WikidataQ57931578 ScholiaQ57931578MaRDI QIDQ464624

Kurt De Grave, Paolo Frasconi, Fabrizio Costa, Luc De Raedt

Publication date: 27 October 2014

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1205.3981

zbMATH Keywords

kernel methodsPrologstatistical relational learningdeductive databaseslogical and relational learning

Mathematics Subject Classification ID

Database theory (68P15) Learning and adaptive systems in artificial intelligence (68T05) Logic programming (68N17)

Related Items (7)

Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment ⋮ Unnamed Item ⋮ An expressive dissimilarity measure for relational clustering using neighbourhood trees ⋮ kProbLog: an algebraic Prolog for machine learning ⋮ A gentle introduction to deep learning for graphs ⋮ kLog ⋮ kProbLog: An Algebraic Prolog for Kernel Programming

Uses Software

• LIBSVM
• ProbLog
• YAP-Prolog
• FACTORIE
• gSpan
• Aleph

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Structured machine learning: the next ten years
• Convex multi-task feature learning
• A parametric filtering algorithm for the graph isomorphism problem
• Isomorphism of graphs of bounded valence can be tested in polynomial time
• Top-down induction of first-order logical decision trees
• Solving the multiple instance problem with axis-parallel rectangles.
• Fast learning of relational kernels
• Relational retrieval using a combination of path-constrained random walks
• Learning to construct knowledge bases from the World Wide Web
• Probabilistic inductive logic programming. Theory and applications
• Markov logic networks
• The YAP Prolog system
• On the implementation of the probabilistic logic programming language ProbLog
• Transforming Graph Data for Statistical Relational Learning
• Large-Scale Machine Learning with Stochastic Gradient Descent
• Logical and Relational Learning
• Kernels for Structured Data
• Feature Discovery with Type Extension Trees
• Improving Prolog programs: Refactoring for Prolog

This page was built for publication: kLog: a language for logical and relational learning with kernels