This paper presents an application of stochastic logic programs (SLPs) to learning probabilistic logic rules in protein fold prediction. We apply SLP parameter estimation algorithm to a previous study in which rules have been learned by inductive logic programming (ILP). On the basis of experiments, we demonstrate that probabilistic ILP approaches (eg. SLPs) have advantages for solving multi-cl...

Current microprocessors exploit high levels of instruction level parallelism ILP through techniques such as multiple issue dynamic scheduling and non blocking reads This paper presents the rst detailed analysis of the impact of such processors on shared memory multiprocessors using a detailed execution driven simulator Using this analysis we also exam ine the validity of common direct execution...

Integrated Layer Processing (ILP) has several limitations on current system environments. Performance evaluations show that the main benefits from ILP are rather a reduced number of memory accesses than an improved cache hit rate. Data manipulation characteristics have significant impact on the cache behavior and on the achievable performance gain of ILP. Future protocol characteristics and eff...

In this thesis we describe a novel approach to application of Evolutionary Algorithms (EAs) into the field of Inductive Logic Programming (ILP). One of the main issues of ILP is the time complexity that comes out of the large size of ILP search space. Improving the search efficiency is therefore the main objective of this thesis. To reach this, EAs were used in this work as they have proven to ...

Inductive Logic Programming (ILP) is a Machine Learning research field that has been quite successful in knowledge discovery in relational domains. ILP systems use a set of pre-classified examples (positive and negative) and prior knowledge to learn a theory in which positive examples succeed and the negative examples fail. In this paper we present a novel ILP system called April, capable of ex...

This paper introduces a new type of application for ILP called Bootstrapped Learning (BL). BL brings several challenges to ILP, including the need to (a) automate the “ILP setup” problem, (b) exploit the fact that a well-meaning teacher is providing pedagogically chosen examples and may be offering hints, (c) deal with small numbers of training examples and sometimes no explicit negative exampl...

The use of background knowledge and the adoption of Horn clausal logic as a knowledge representation and reasoning framework are the distinguishing features of Inductive Logic Programming (ILP) with respect to other approaches to concept learning. We argue that ILP can not ignore the latest developments in Knowledge Engineering such as ontologies and formalisms based on Description Logics. In t...