New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Bayesian inductive logic programming
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Relational instance-based learning with lists and terms
Machine Learning - Special issue on inducive logic programming
Propositionalization approaches to relational data mining
Relational Data Mining
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Learning Nonrecursive Definitions of Relations with LINUS
EWSL '91 Proceedings of the European Working Session on Machine Learning
A Framework for Learning Rules from Multiple Instance Data
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Distance Between Herbrand Interpretations: A Measure for Approximations to a Target Concept
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
A Framework for Defining Distances Between First-Order Logic Objects
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Stochastic Propositionalization of Non-determinate Background Knowledge
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Logic and Learning
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Text classification using string kernels
The Journal of Machine Learning Research
Cyclic pattern kernels for predictive graph mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Margin-based first-order rule learning
Machine Learning
An Efficient Algorithm for Computing Kernel Function Defined with Anti-unification
Inductive Logic Programming
Kernel Functions Based on Derivation
New Frontiers in Applied Data Mining
Multi-class protein fold recognition using large margin logic based divide and conquer learning
Proceedings of the KDD-09 Workshop on Statistical and Relational Learning in Bioinformatics
kFOIL: learning simple relational kernels
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A simple and effective method for incorporating advice into kernel methods
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning Large Margin First Order Decision Lists for Multi-Class Classification
DS '09 Proceedings of the 12th International Conference on Discovery Science
Combining clauses with various precisions and recalls to produce accurate probabilistic estimates
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Using ILP to construct features for information extraction from semi-structured text
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Learning with kernels and logical representations
Probabilistic inductive logic programming
Multi-Class protein fold recognition using large margin logic based divide and conquer learning
ACM SIGKDD Explorations Newsletter
Multitask Kernel-based Learning with Logic Constraints
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Learning with semantic kernels for clausal knowledge bases
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Induction of robust classifiers for web ontologies through kernel machines
Web Semantics: Science, Services and Agents on the World Wide Web
Scalable relation prediction exploiting both intrarelational correlation and contextual information
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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In this paper we explore a topic which is at the intersection of two areas of Machine Learning: namely Support Vector Machines (SVMs) and Inductive Logic Programming (ILP). We propose a general method for constructing kernels for Support Vector Inductive Logic Programming (SVILP). The kernel not only captures the semantic and syntactic relational information contained in the data but also provides the flexibility of using arbitrary forms of structured and non-structured data coded in a relational way. While specialised kernels have been developed for strings, trees and graphs our approach uses declarative background knowledge to provide the learning bias. The use of explicitly encoded background knowledge distinguishes SVILP from existing relational kernels which in ILP-terms work purely at the atomic generalisation level. The SVILP approach is a form of generalisation relative to background knowledge, though the final combining function for the ILP-learned clauses is an SVM rather than a logical conjunction. We evaluate SVILP empirically against related approaches, including an industry-standard toxin predictor called TOPKAT. Evaluation is conducted on a new broad-ranging toxicity dataset (DSSTox). The experimental results demonstrate that our approach significantly outperforms all other approaches in the study.