Support Vector Inductive Logic Programming
Support Vector Inductive Logic Programming
复制标题
支持向量归纳逻辑编程
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
M. Sternberg
中科院分区：
文献类型：
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作者：
S. Muggleton;H. Lodhi;A. Amini;M. Sternberg
在本文中，我们探讨了一个主题，它是机器学习的两个领域的交集：即支持向量机（svm）和归纳逻辑编程（ILP）。提出了一种构造支持向量归纳逻辑规划（SVILP）核函数的通用方法。内核不仅捕获数据中包含的语义和句法关系信息，而且还提供了使用以关系方式编码的任意形式的结构化和非结构化数据的灵活性。虽然已经为字符串、树和图开发了专门的内核，但我们的方法使用声明性背景知识来提供学习偏差。使用显式编码的背景知识将SVILP与现有的关系内核区分开来，后者在ilp术语中纯粹在原子泛化级别上工作。SVILP方法是一种相对于背景知识的泛化形式，尽管最终的组合函数是一个支持向量机，而不是一个逻辑连接。我们根据相关方法对SVILP进行经验评估，包括一个称为TOPKAT的行业标准毒素预测器。评估是在一个新的广泛的毒性数据集（DSSTox）上进行的。实验结果表明，我们的方法明显优于研究中的所有其他方法。
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 provides the flexibility of using arbitrary forms of structured and non-structured data coded in a relational way. While specialised kernels have been developed for strings, trees and graphs our approach uses declarative background knowledge to provide the learning bias. The use of explicitly encoded background knowledge distinguishes SVILP from existing relational kernels which in ILP-terms work purely at the atomic generalisation level. The SVILP approach is a form of generalisation relative to background knowledge, though the final combining function for the ILP-learned clauses is an SVM rather than a logical conjunction. We evaluate SVILP empirically against related approaches, including an industry-standard toxin predictor called TOPKAT. Evaluation is conducted on a new broad-ranging toxicity dataset (DSSTox). The experimental results demonstrate that our approach significantly outperforms all other approaches in the study.