What is included with this book?
Propositionalization for Clustering Symbolic Relational Descriptions | p. 1 |
Efficient and Effective Induction of First Order Decision Lists | p. 17 |
Learning with Feature Description Logics | p. 32 |
An Empirical Evaluation of Bagging in Inductive Logic Programming | p. 48 |
Kernels for Structured Data | p. 66 |
Experimental Comparison of Graph-Based Relational Concept Learning with Inductive Logic Programming Systems | p. 84 |
Autocorrelation and Linkage Cause Bias in Evaluation of Relational Learners | p. 101 |
Learnability of Description Logic Programs | p. 117 |
1BC2: A True First-Order Bayesian Classifier | p. 133 |
RSD: Relational Subgroup Discovery through First-Order Feature Construction | p. 149 |
Mining Frequent Logical Sequences with SPIRIT-L[superscript o]G | p. 166 |
Using Theory Completion to Learn a Robot Navigation Control Program | p. 182 |
Learning Structure and Parameters of Stochastic Logic Programs | p. 198 |
A Novel Approach to Machine Discovery: Genetic Programming and Stochastic Grammars | p. 207 |
Revision of First-Order Bayesian Classifiers | p. 223 |
The Applicability to ILP of Results Concerning the Ordering of Binomial Populations | p. 238 |
Compact Representation of Knowledge Bases in ILP | p. 254 |
A Polynomial Time Matching Algorithm of Structured Ordered Tree Patterns for Data Mining from Semistructured Data | p. 270 |
A Genetic Algorithms Approach to ILP | p. 285 |
Experimental Investigation of Pruning Methods for Relational Pattern Discovery | p. 301 |
Noise-Resistant Incremental Relational Learning Using Possible Worlds | p. 317 |
Lattice-Search Runtime Distributions May Be Heavy-Tailed | p. 333 |
Learning in Rich Representations: Inductive Logic Programming and Computational Scientific Discovery | p. 346 |
Author Index | p. 351 |
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