Inductive Logic Programming
Editat de Stan Matwin, Claude Sammuten Limba Engleză Paperback – 12 feb 2003
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Specificații
ISBN-13: 9783540005674
ISBN-10: 3540005676
Pagini: 368
Ilustrații: X, 358 p.
Dimensiuni: 155 x 235 x 20 mm
Greutate: 0.56 kg
Ediția:2003
Editura: Springer
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540005676
Pagini: 368
Ilustrații: X, 358 p.
Dimensiuni: 155 x 235 x 20 mm
Greutate: 0.56 kg
Ediția:2003
Editura: Springer
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
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 of Bagging in Inductive Logic Programming.- Kernels for Structured Data.- Experimental Comparison of Graph-Based Relational Concept Learning with Inductive Logic Programming Systems.- Autocorrelation and Linkage Cause Bias in Evaluation of Relational Learners.- Learnability of Description Logic Programs.- 1BC2: A True First-Order Bayesian Classifier.- RSD: Relational Subgroup Discovery through First-Order Feature Construction.- Mining Frequent Logical Sequences with SPIRIT-LoG.- Using Theory Completion to Learn a Robot Navigation Control Program.- Learning Structure and Parameters of Stochastic Logic Programs.- A Novel Approach to Machine Discovery: Genetic Programming and Stochastic Grammars.- Revision of First-Order Bayesian Classifiers.- The Applicability to ILP of Results Concerning the Ordering of Binomial Populations.- Compact Representation of Knowledge Bases in ILP.- A Polynomial Time Matching Algorithm of Structured Ordered Tree Patterns for Data Mining from Semistructured Data.- A Genetic Algorithms Approach to ILP.- Experimental Investigation of Pruning Methods for Relational Pattern Discovery.- Noise-Resistant Incremental Relational Learning Using Possible Worlds.- Lattice-Search Runtime Distributions May Be Heavy-Tailed.- Invited Talk Abstracts.- Learning in Rich Representations: Inductive Logic Programming and Computational Scientific Discovery.
Caracteristici
Includes supplementary material: sn.pub/extras