FOIL is a learning system that constructs Horn clause programs from examples. This paper summarises the development of FOIL from 1989 up to early 1993 and evaluates its effectiveness on a non-trivial sequence of learning tasks taken from a Prolog programming text. Although many of these tasks are handled reasonably well, the experiment highlights some weaknesses of the current implementation. Areas for further research are identified.
foil is a rst-order learning system that uses information in a collection of relations to construct theories expressed in a dialect of Prolog. This paper provides an overview of the principal ideas and methods used in the current v ersion of the system, including two recent additions. We present examples of tasks tackled by foil and of systems that adapt and extend its approach.
Abstract. The ability to locate promoters within a section of DNA is known to be a very difficult and very important task in DNA analysis. We document an approach that incorporates the concept of DNA as a complex molecule using several models of its physico-chemical properties. A support vector machine is trained to recognise promoters by their distinctive physical and chemical properties. We demonstrate that by combining models, we can improve upon the classification accuracy obtained with a single model. We also show that by examining how the predictive accuracy of these properties varies over the promoter, we can reduce the number of attributes needed. Finally, we apply this method to a real-world problem. The results demonstrate that such an approach has significant merit in its own right. Furthermore, they suggest better results from a planned combined approach to promoter prediction using both physicochemical and sequence based techniques.
FOIL is a system for inducing function-free Horn clause definitions of relations from example and extensionally defined background relations. It demonstrates the successful application of a general to specific approach to clause induction using heuristically guided search. This paper describes the current version of FOIL, assesses its performance and notes areas for improvement. The successful application of similar methods in other systems is reviewed to demonstrate their general utility.
Multiple sequence alignment is a crucial technique for many fields of computational biology and remains a difficult task. Combining several different alignment techniques often leads to the best results in practice. Within this paper we present MAUSA (Multiple Alignment Using Simulated Annealing) and show that the conceptually simple approach of simulated annealing, when combined with a recent development in solving the aligning alignments problem, produces results which are competitive and in some cases superior to established methods for sequence alignment. We show that the application of simulated annealing to effective guide tree selection improves the quality of the alignments produced. In addition, we apply a method for the automatic assessment of alignment quality and show that in scenarios where MAUSA is selected as producing the best alignment from a suite of approaches (approximately 10% of test cases), it produces an average 5% (p = 0.005, Wilcoxon sign-rank test) improvement in quality.
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