作者: Claude Sammut , Stan Matwin
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摘要: Contributed Papers.- Propositionalization for Clustering Symbolic Relational Descriptions.- Efficient and Effective Induction of First Order Decision Lists.- Learning with Feature Description Logics.- An Empirical Evaluation Bagging in Inductive Logic Programming.- Kernels Structured Data.- Experimental Comparison Graph-Based Concept Programming Systems.- Autocorrelation Linkage Cause Bias Learners.- Learnability Programs.- 1BC2: A True First-Order Bayesian Classifier.- RSD: Subgroup Discovery through Construction.- Mining Frequent Logical Sequences SPIRIT-LoG.- Using Theory Completion to Learn a Robot Navigation Control Program.- Structure Parameters Stochastic Novel Approach Machine Discovery: Genetic Grammars.- Revision Classifiers.- The Applicability ILP Results Concerning the Ordering Binomial Populations.- Compact Representation Knowledge Bases ILP.- Polynomial Time Matching Algorithm Ordered Tree Patterns Data from Semistructured Algorithms Investigation Pruning Methods Pattern Discovery.- Noise-Resistant Incremental Possible Worlds.- Lattice-Search Runtime Distributions May Be Heavy-Tailed.- Invited Talk Abstracts.- Rich Representations: Computational Scientific Discovery.