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We describe an efficient implementation (MRDTL-2) of the Multi-relational decision tree learning (MRDTL) algorithm [19] which in turn was based on a proposal by Knobbe et al. [15] We describe some simple techniques for speeding up the calculation of sufficient statistics for decision trees and related hypothesis classes from multi-relational data. Because(More)
We present and implement an efficient algorithm for performing nearest-neighbor queries in topological spaces that usually arise in the context of motion planning. Our approach extends the Kd tree-based ANN algorithm , which was developed by Arya and Mount for Eu-clidean spaces. We argue the correctness of the algorithm and illustrate its efficiency through(More)
We describe experiments with an implementation of Multi-relational decision tree learning (MRDTL) algorithm for induction of decision trees from relational databases using an approach proposed by Knobbe et al. [1999a]. Our results show that the performance of MRDTL is competitive with that of other algorithms for learning classifiers from multiple relations(More)
Gathering huge amounts of complex information (data and knowledge) is very common nowadays. This calls for the necessity to represent, store and manipulate complex information (e.g. detect correlations and patterns, discover explanations, construct predictive models etc.). Furthermore, being autonomously maintained, data can change in time or even change(More)
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