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

大炮

胶水

AER

残酷的地方

Efficient and Effective Induction of First Order Decision Listsntation for learning concepts that include exceptions, such as language constructs, previous systems suffered from limitations that we seek to overcome in BUFOIDL. We present experiments comparing BUFOIDL to previous work in the area, demonstrating the system’s potential.

CIS

prediabetes

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Kernels for Structured Datalue data. Only recently have researchers started investigating kernels for structured data. This paper describes how kernel definitions can be simplified by identifying the structure of the data and how kernels can be defined on this structure. We propose a kernel for structured data, prove that it

和谐

Experimental Comparison of Graph-Based Relational Concept Learning with Inductive Logic Programming , we use the conceptual graphs representation. Conceptual graphs have a standard translation from graphs into logic. In this way, we introduce less bias during the translation process. We experiment with different types of domains. First, we show our experiments with an artificial domain to describe

悦耳

Autocorrelation and Linkage Cause Bias in Evaluation of Relational Learnersn to greatly overestimate the accuracy of induced models on test sets. We identify these characteristics, define quantitative measures of their severity, and explain how they produce this bias. We show how linkage and autocorrelation affect estimates of model accuracy by applying FOIL to synthetic d

不溶解

Learnability of Description Logic Programshe normal bias used in ILP as it allows the use of all quantified variables in the body of a clause. It also has at-least and at-most restrictions to access the amount of indeterminism of relations. From a complexity point of view Carin-. allows to handle the difficult indeterminate relations effici