Fast learning of relational kernels

DOI10.1007/s10994-009-5163-1zbMath1470.68129OpenAlexW2110668981MaRDI QIDQ1959554

Andrea Passerini, Luc De Raedt, Niels Landwehr, Paolo Frasconi

Publication date: 7 October 2010

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-009-5163-1

zbMATH Keywords

kernel methodsinductive logic programmingmulti-task learningstatistical relational learningkernel learningdynamic propositionalization

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) (46E22) Logic programming (68N17)

Related Items

Unnamed Item ⋮ Bridging logic and kernel machines ⋮ Fast learning of relational kernels ⋮ kLog: a language for logical and relational learning with kernels ⋮ Type extension trees for feature construction and learning in relational domains ⋮ Semantic-based regularization for learning and inference ⋮ Unnamed Item ⋮ Unnamed Item

Uses Software

• FORS
• Pegasos
• kFOIL
• gBoost
• nFOIL

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Pegasos: primal estimated sub-gradient solver for SVM
• First order regression
• Kernels and distances for structured data
• Margin-based first-order rule learning
• Top-down induction of first-order logical decision trees
• Dealing with large diagonals in kernel matrices
• Robust trainability of single neurons
• Fast learning of relational kernels
• gBoost: a mathematical programming approach to graph classification and regression
• Large margin classification using the perceptron algorithm
• Probabilistic inductive logic programming. Theory and applications
• On the mathematical foundations of learning
• Logical and Relational Learning
• Feature Discovery with Type Extension Trees
• Inductive Logic Programming: Theory and methods
• 10.1162/153244302760200687
• 10.1162/153244303321897681
• Inductive Logic Programming
• A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines
• Convexity, Classification, and Risk Bounds
• Choosing multiple parameters for support vector machines