摘要
对图等内含结构信息的数据进行学习,是机器学习领域的一个重要问题.核方法是解决此类问题的一种有效技术.文中针对分子图分类问题,基于Swamidass等人的工作,提出用于图分类的组合维核方法.该方法首先构建融合一维信息的二维核来刻画分子化学特征,然后基于分子力学的相关知识,利用几何信息构建三维核来刻画分子物理性质.在此基础上对不同维度的核进行集成,通过求解二次约束二次规划问题来获得最优核组合.实验结果表明,文中方法比现有技术具有更好的性能.
Learning from structured data, such as graphs, is an important problem in machine learning. Kernel method is regarded as a powerful solution to such a problem. This paper focuses on molecular graph classification and, following Swamidass et al.＇s work, proposes an improved method using combo-dimensional kernels. The proposed method first constructs 2D kernels combined with 1D information to describe chemical characteristics, and to describe physical characteristics, it then constructs 3D kernels based on geometrical information and related molecular mechanics knowledge. Furthermore, inspired by ensemble learning with multiple dimensions, the method finds the optimal kernel combination by quadratically constrained quadratic programming. Experiments show that the proposed method outperforms existing algorithms.
出处
《计算机学报》
EI
CSCD
北大核心
2009年第5期946-952,共7页
Chinese Journal of Computers
基金
国家自然科学基金(60635030,60721002)
江苏省自然科学基金(BK2008018)
江苏省333工程资助
关键词
机器学习
图分类
核方法
结构信息
集成学习
machine learning
graph classification
kernel methods
structure information
ensemble learning
