Towards Inductive Support Logic Programming

Towards Combining Inductive Logic Programming with Bayesian Networks

A Methodology for Rst Order Learning: a Case Study

Many well-known learning systems like CN2 and C4.5 use an attribute-value representation for the examples. In Inductive Logic Programming (ILP) a rst-order logical formalism is used, which is a more expressive representation. We will introduce a 5-step methodology to upgrade an existing propositional learner towards a rst-order learning system. The system ICL (Inductive Classiication Logic) is ...

A Comparative Study of Inductive Logic Programming Methods for Software Fault Prediction

We evaluate inductive logic programming (ILP) methods for predicting fault density in C++ classes. In this problem, each training example is a C++ class deenition, represented as a calling tree, and labeled as \pos-itive" ii faults (i.e., errors) were discovered in its implementation. We compare two ILP systems, FOIL and FLIPPER, and explore the reasons for their diiering performance, using bot...

Support Vector Inductive Logic Programming

In this paper we explore a topic which is at the intersection of two areas of Machine Learning: namely Support Vector Machines (SVMs) and Inductive Logic Programming (ILP). We propose a general method for constructing kernels for Support Vector Inductive Logic Programming (SVILP). The kernel not only captures the semantic and syntactic relational information contained in the data but also provi...

kFOIL: Learning Simple Relational Kernels

A novel and simple combination of inductive logic programming with kernel methods is presented. The kFOIL algorithm integrates the well-known inductive logic programming system FOIL with kernel methods. The feature space is constructed by leveraging FOIL search for a set of relevant clauses. The search is driven by the performance obtained by a support vector machine based on the resulting kern...