Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Relational Data Mining
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
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An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Applying ILP to Diterpene Structure Elucidation from 13C NMR Spectra
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Kernels and Distances for Structured Data
Machine Learning
Margin-based first-order rule learning
Machine Learning
Querying and Merging Heterogeneous Data by Approximate Joins on Higher-Order Terms
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Exploiting propositionalization based on random relational rules for semi-supervised learning
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Clustering relational data based on randomized propositionalization
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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In this paper we present a novel and general framework based on concepts of relational algebra for kernel-based learning over relational schema. We exploit the notion of foreign keys to define a new attribute that we call instance-set and we use this type of attribute to define a tree like structured representation of the learning instances. We define kernel functions over relational schemata which are instances of $\Re$-Convolution kernels and use them as a basis for a relational instance-based learning algorithm. These kernels can be considered as being defined over typed and unordered trees where elementary kernels are used to compute the graded similarity between nodes. We investigate their formal properties and evaluate the performance of the relational instance-based algorithm on a number of relational data sets.