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1.
Learning Logical Definitions from Relations   总被引:49,自引:13,他引:36  
This paper describes FOIL, a system that learns Horn clauses from data expressed as relations. FOIL is based on ideas that have proved effective in attribute-value learning systems, but extends them to a first-order formalism. This new system has been applied successfully to several tasks taken from the machine learning literature.  相似文献   

2.
梁平  杨宪泽 《计算机科学》2011,38(6):177-179
在面对服务架构中,业务逻辑由各种服务组件组合完成,不同服务组件完成不同业务流程的业务逻辑。面对不断变化和发展的商业环境,服务的组合性需要具有灵活性和可靠性。虽然目前已有多种方法能够优化和改善面对服务系统,但是针对服务编排自动化的应用和研究较少,这主要归因于复杂的业务逻辑和繁琐的服务编排工具。介绍一种采用一阶内涵逻辑语言(FOIL)为业务流程建立其业务逻辑的FOIL公式,通过计算FOIL公式,自动生成WS-BPEL结构以动态绑定已有的服务组件,最终完成自动服务编排。Tarski的真值理论证明这种方法具有实际应用价值。  相似文献   

3.
Web知识规则提取的FOIL算法改进   总被引:3,自引:0,他引:3  
将一阶学习的FOIL算法应用到Web知识规则的提取是当前学习Web知识所普遍采用的方法.本文在FOIL算法的基础上进行了改进,提出了基于网页间联系的新的路径学习算法,使得原算法的稳定性和精确度都明显提高。  相似文献   

4.
一个不受常量序限制的归纳逻辑程序设计算法   总被引:2,自引:0,他引:2  
张润琦  陈小平 《软件学报》1999,10(8):868-876
文章分析了FOIL(first-orderinductivelearner)递归谓词学习算法理论上的不足以及由此导致的应用范围的局限,并通过两个例子给予详细说明.为了克服这一缺陷,文章引入了反映递归规则集R与实例空间E本质关系的实例图H(R.E)和实例序的概念,奠定了算法的理论基础.在此基础上,给出了基于实例图的FOILPlus算法.算法通过对悬例、悬弧的操作把握住实例序,自然而然地防止了病态递归规则的产生,从而保证了FOILPlus可以不受常量序限制地完成学习任务;同时,算法的时空复杂度较之FOIL算法没有增加.FOILPlus算法已经编程实现,并用它尝试了两个FOIL学习失败的递归任务,都获得了成功.  相似文献   

5.
文章分析了FOIL(first-order inductive)递归谓词学习算法理论上的不足以及由此导致的应用范围的局限,并通过两个例子给予详细说明.为了克服这一缺陷,文章引入了反映递归规则集R与实例空间E本质关系的实例图H(R,E)和实例序的概念,奠定了算法的理论基础.在此基础上,给出了基于实例图的FOILPlus算法.算法通过对悬例、悬弧的操作把握住实例序,自然而然的防止了病态递归规则的产生,从而保证FOILPlus可以不受常量序限制地完成学习任务;同时,算法的时空复杂度较之FOIL算法没有增加.FOILPlus算法已经编程实现,并用它尝试了两个FOIL学习失败的递归任务,都获得了成功.  相似文献   

6.
While recent research on rule learning has focused largely on finding highly accurate hypotheses, we evaluate the degree to which these hypotheses are also simple, that is small. To realize this, we compare well-known rule learners, such as CN2, RIPPER, PART, FOIL and C5.0 rules, with the benchmark system SL2 that explicitly aims at computing small rule sets with few literals. The results show that it is possible to obtain a similar level of accuracy as state-of-the-art rule learners using much smaller rule sets.  相似文献   

7.
近些年来,语义Web和网格计算这两个方向在各自的研究社区分别发展着,这两方面的交叉即语义网格(semantic grid)则是最近一段时间兴起的研究领域.通过给网格附加语义层,能够促进网格自组织的形成.现有的Gnd社区都是使用集中式的、一致性的、可扩充的Ontology库.超越集中式的语义存储是语义网格发展面临的最大挑战之一.针对网格社区间的Ontology异构性这个问题,提出了一种多策略的Ontology匹配学习方法.它使用多种分类方法来学习Ontology之间的匹配:使用一般的基于统计的分类方法来发现数据实例内部的分类特征;或者使用基于一阶逻辑的学习算法FOIL来发现数据实例之间的语义联系.在单个方法预测的基础上,匹配系统使用称之为最突出的冠军的匹配委员会方法来集成分类结果.实验表明在现实的知识领域中,系统能达到很高的匹配精度.  相似文献   

8.
Inductive Logic Programming (ILP) studies learning from examples, within the framework provided by clausal logic. ILP has become a popular subject in the field of data mining due to its ability to discover patterns in relational domains. Several ILP-based concept discovery systems are developed which employs various search strategies, heuristics and language pattern limitations. LINUS, GOLEM, CIGOL, MIS, FOIL, PROGOL, ALEPH and WARMR are well-known ILP-based systems. In this work, firstly introductory information about ILP is given, and then the above-mentioned systems and an ILP-based concept discovery system called C2D are briefly described and the fundamentals of their mechanisms are demonstrated on a running example. Finally, a set of experimental results on real-world problems are presented in order to evaluate and compare the performance of the above-mentioned systems.  相似文献   

9.
Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Moreover, high predictive accuracy and good interpretability of the results are two key measures of a classification model. More studies have shown that single model-based classification methods may not be good enough to achieve a satisfactory result. To obtain more accurate predictive results, we present a novel hybrid model-based learning system, which integrates the supervised and unsupervised techniques for predicting customer behaviour. The system combines a modified k-means clustering algorithm and a classic rule inductive technique (FOIL).Three sets of experiments were carried out on telecom datasets. One set of the experiments is for verifying that the weighted k-means clustering can lead to a better data partitioning results; the second set of experiments is for evaluating the classification results, and comparing it to other well-known modelling techniques; the last set of experiment compares the proposed hybrid-model system with several other recently proposed hybrid classification approaches. We also performed a comparative study on a set of benchmarks obtained from the UCI repository. All the results show that the hybrid model-based learning system is very promising and outperform the existing models.  相似文献   

10.
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