Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
On the Algorithmic Implementation of Stochastic Discrimination
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Machine Learning
Phase Transitions in Relational Learning
Machine Learning
Learning Logical Definitions from Relations
Machine Learning
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Applying ILP to Diterpene Structure Elucidation from 13C NMR Spectra
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Carcinogenesis Predictions Using ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Propositionalization-based relational subgroup discovery with RSD
Machine Learning
Scaling up semi-supervised learning: an efficient and effective LLGC variant
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
An empirical evaluation of bagging in inductive logic programming
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Clustering relational data based on randomized propositionalization
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Semi-Supervised Learning
Kernels over relational algebra structures
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A spectrum-based support vector algorithm for relational data semi-supervised classification
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
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In this paper we investigate an approach to semi-supervised learning based on randomized propositionalization, which allows for applying standard propositional classification algorithms like support vector machines to multi-relational data. Randomization based on random relational rules can work both with and without a class attribute and can therefore be applied simultaneously to both the labeled and the unlabeled portion of the data present in semi-supervised learning. An empirical investigation compares semi-supervised propositionalization to standard propositionalization using just the labeled data portion, as well as to a variant that also just uses the labeled data portion but includes the label information in an attempt to improve the resulting propositionalization. Preliminary experimental results indicate that propositionalization generated on the full dataset, i.e. the semisupervised approach, tends to outperform the other two more standard approaches.