Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Making large-scale support vector machine learning practical
Advances in kernel methods
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Relational instance-based learning with lists and terms
Machine Learning - Special issue on inducive logic programming
Machine Learning - Special issue on inducive logic programming
A polynomial time computable metric between point sets
Acta Informatica
Algorithmic Program DeBugging
Distance based approaches to relational learning and clustering
Relational Data Mining
Propositionalization approaches to relational data mining
Relational Data Mining
Application of Cascade Correlation Networks for Structures toChemistry
Applied Intelligence
Support Vector Machines and the Bayes Rule in Classification
Data Mining and Knowledge Discovery
Learning Logical Definitions from Relations
Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Framework for Defining Distances Between First-Order Logic Objects
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Thesis: clustering and instance based learning in first order logic
AI Communications
Logic and Learning
Text classification using string kernels
The Journal of Machine Learning Research
The Journal of Machine Learning Research
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Matrix with Semidefinite Programming
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
Extensions of marginalized graph kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Rational Kernels: Theory and Algorithms
The Journal of Machine Learning Research
Kernels and Distances for Structured Data
Machine Learning
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Weighted decomposition kernels
ICML '05 Proceedings of the 22nd international conference on Machine learning
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Prediction of Ordinal Classes Using Regression Trees
Fundamenta Informaticae - Intelligent Systems
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting
The Journal of Machine Learning Research
Constructing Programs from Example Computations
IEEE Transactions on Software Engineering
kFOIL: learning simple relational kernels
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Towards learning stochastic logic programs from proof-banks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
nFOIL: integrating Naïve Bayes and FOIL
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Representing sentence structure in hidden Markov models for information extraction
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Kernels on prolog ground terms
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning with feature description logics
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Support vector inductive logic programming
DS'05 Proceedings of the 8th international conference on Discovery Science
Learning with kernels and logical representations
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Multitask Kernel-based Learning with Logic Constraints
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Speeding up inference in statistical relational learning by clustering similar query literals
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
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In this chapter, we describe a view of statistical learning in the inductive logic programming setting based on kernel methods. The relational representation of data and background knowledge are used to form a kernel function, enabling us to subsequently apply a number of kernel-based statistical learning algorithms. Different representational frameworks and associated algorithms are explored in this chapter. In kernels on Prolog proof trees, the representation of an example is obtained by recording the execution trace of a program expressing background knowledge. In declarative kernels, features are directly associated with mereotopological relations. Finally, in kFOIL, features correspond to the truth values of clauses dynamically generated by a greedy search algorithm guided by the empirical risk.