Abstract
Inductive Logic Programming (ILP) is a popular approach for learning rules for classification tasks. An important question is how to combine the individual rules to obtain a useful classifier. In some instances, converting each learned rule into a binary feature for a Bayes net learner improves the accuracy compared to the standard decision list approach [3,4,14]. This results in a two-step process, where rules are generated in the first phase, and the classifier is learned in the second phase. We propose an algorithm that interleaves the two steps, by incrementally building a Bayes net during rule learning. Each candidate rule is introduced into the network, and scored by whether it improves the performance of the classifier. We call the algorithm SAYU for Score As You Use. We evaluate two structure learning algorithms Naïve Bayes and Tree Augmented Naïve Bayes. We test SAYU on four different datasets and see a significant improvement in two out of the four applications. Furthermore, the theories that SAYU learns tend to consist of far fewer rules than the theories in the two-step approach. © Springer-Verlag Berlin Heidelberg 2005.

Abstract
Parallel logic programming (PLP) systems are sophisticated examples of symbolic computing systems. PLP systems address problems such as allocating dynamic memory, scheduling irregular computations, and managing different types of implicit parallelism. Most PLP systems have been developed for bus-based architectures. However, the complexity of PLP systems and the large amount of data they process raise the question of whether logic programming systems can still achieve good performance on modern scalable architectures, such as distributed shared-memory (DSM) systems. In this work we use execution-driven simulation of a cache-coherent DSM architecture to investigate the performance of Andorra-I, a state-of-the-art PLP system, on a modern multiprocessor. The results of this simulation show that Andorra-I exhibits reasonable running time performance, but it does not scale well. Our detailed analysis of cache misses and their sources expose several opportunities for improvements in Andorra-I. Based on this analysis, we modify Andorra-I using a set of simple techniques that led to significantly better running time and scalability. These results suggest that Andorra-I can and should perform well on modern multiprocessors. Furthermore, as Andorra-I shares its main data structures with several PLP systems, we conclude that the methodology and techniques used in our work can greatly benefit these other PLP systems. © 2000 Academic Press.

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Statistical relational learning (SRL) constructs probabilistic models from relational databases. A key capability of SRL is the learning of arcs (in the Bayes net sense) connecting entries in different rows of a relational table, or in different tables. Nevertheless, SRL approaches currently are constrained to use the existing database schema. For many database applications, users find it profitable to define alternative "views" of the database, in effect defining new fields or tables. Such new fields or tables can also be highly useful in learning. We provide SRL with the capability of learning new views.

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