Titlebook: Inductive Logic Programming; 12th International C Stan Matwin,Claude Sammut Conference proceedings 2003 Springer-Verlag Berlin Heidelberg 2

山羊

1BC2: A True First-Order Bayesian Classifiernting first-order features are generated exhaustively within a given feature bias, but during learning rather than as a pre-processing step. In this paper we describe 1BC2, which learns from structured data by fitting various parametric distributions over sets and lists to the data.We evaluate the f

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贸易

Mining Frequent Logical Sequences with SPIRIT-LoGse efficiency of basic sequence mining algorithms, generally based on a levelwise approach, more recent algorithms try to introduce some constraints to prune the search space during the discovery process. Nevertheless, existing algorithms are actually limited to extract frequent sequences made up of

nutrition

Using Theory Completion to Learn a Robot Navigation Control Program the non-observational predicate learning setting. This work utilized 1) extraction-case abduction; 2) the simultaneous completion of two, mutually related predicates; and 3) positive observations only learning. Given time-trace observations of a robot successfully navigating a model office and othe

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palliative-care

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emission

Organization

Compact Representation of Knowledge Bases in ILPntered representation can cause a lot of redundancy. For instance, when learning from episodes (e.g. from games), the knowledge base contains consecutive states of a world. Each state is usually described completely even though consecutive states may differ only slightly. Similar redundancies occur

勤劳

A Polynomial Time Matching Algorithm of Structured Ordered Tree Patterns for Data Mining from Semistnsider a problem of deciding whether or not a term tree . matches a tree ., that is, . is obtained from . by substituting some trees for variables in .. Such a problem is called a membership problem for . and .. Given a term tree . and a tree ., we present an .(.) time algorithm of solving the membe