An ILP Refinement Operator for Biological Grammar Learning
An ILP Refinement Operator for Biological Grammar Learning
复制标题
用于生物语法学习的 ILP 细化算子
DOI：
10.1007/978-3-540-73847-3_24
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
S. Topp
中科院分区：
文献类型：
--
作者：
Daniel Fredouille;Christopher H. Bryant;C. Jayawickreme;S. Jupe;S. Topp
我们感兴趣的是使用归纳逻辑编程（ILP）来推断表示生物序列集的语法。我们称之为生物语法。ILP系统非常适合这项任务的意义上说，生物语法已表示为逻辑程序使用的定子句语法或字符串变量语法形式主义。然而，ILP系统生成生物语法的速度已经被证明是一个瓶颈。本文提出了一种新的细化运算符的实现，专门推断生物文法与ILP技术。与使用经典的细化算子相比，这种实现显着加快了推理时间：在$\frac{4}{5}$的实验中观察到大于5倍的时间增益，最大观察到的增益超过300倍。
We are interested in using Inductive Logic Programming ( ILP ) to infer grammars representing sets of biological sequences. We call these biological grammars. ILP systems are well suited to this task in the sense that biological grammars have been represented as logic programs using the Definite Clause Grammar or the String Variable Grammar formalisms. However, the speed at which ILP systems can generate biological grammars has been shown to be a bottleneck. This paper presents a novel refinement operator implementation, specialised to infer biological grammars with ILP techniques. This implementation is shown to significantly speed-up inference times compared to the use of the classical refinement operator: time gains larger than 5-fold were observed in $\frac{4}{5}$ of the experiments, and the maximum observed gain is over 300-fold.
影响因子：
14.9
作者：
SAKAKIBARA, Y;BROWN, M;HAUSSLER, D
通讯作者：
HAUSSLER, D