Titlebook: Inductive Logic Programming; 9th International Wo Sašo Džeroski,Peter Flach Conference proceedings 1999 Springer-Verlag Berlin Heidelberg 1

ostensible

Sorted Downward Refinement: Building Background Knowledge into a Refinement Operator for Inductive Lprisingly, no serious attempts have been made to account for background knowledge in refinement operators for clauses, even though such operators are one of the most important, prominent and widely-used devices in the field. This paper shows how a sort theory, which encodes taxonomic knowledge, can

lipids

A Strong Complete Schema for Inductive Functional Logic Programmingperational semantics of (FLP) performs a infication (mgu) followed by a replacement, we introduce two main operators in our IFLP schema: a generalisation and an inverse replacement or property of equality. We prove that this schema is . complete in tha way that, given some evidence, it is possible t

炸坏

Gustatory

Combining , and WordNet for the Learning of LR Parsers with Optimal Semantic Constraintsiques based on logic. The presence of lexical semantic tags in the treebank has proved useful for learning semantic constraints which limit the amount of nondeterminism in the parsers. The level of generality of the semantic tags used is of direct importance to that task. We combine the ILP system .

抗原

不能约

Approximate ILP Rules by Backpropagation Neural Network: A Result on Thai Character Recognitiongnition. In such a learning problem, there exist several different classes of examples; there are 77 different Thai characters. Using examples constructed from character images, ILP learns 77 rules each of which defines each character. However, some unseen character images, especially the noisy imag

钉牢

Rule Evaluation Measures: A Unifying Viewsification accuracy. With new tasks being addressed in knowledge discovery, new measures appear. In descriptive knowledge discovery, where induced rules are not primarily intended for classification, new measures used are novelty in clausal and subgroup discovery, and support and confidence in assoc

抑制

高射炮

On Sufficient Conditions for Learnability of Logic Programs from Positive Datast cases, these results involve logic programs in which the “size” of the head yields a bound on the size of the body literals. However, when local variables are present, such a bound on body literal size cannot directly be ensured. The above authors achieve such a restriction using technical notion

pineal-gland

Probabilistic Relational Modelsf reasoning explicitly about entities, and thus cannot represent models over domains where the set of entities and the relations between them are not fixed in advance. As a consequence, Bayesian networks are limited in their ability to model large and complex domains. . are a language for describing