.png){kind=icon}
返回
Polynomial kernel learning for interpolation kernel machines with application to graph classification
.png){kind=icon}
OA
AI
DOI：10.1016/j.patrec.2024.08.022.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Since all training data is interpolated, interpolating classifiers have zero training error. However, recent work provides compelling reasons to investigate these classifiers, including their significance for ensemble methods. Interpolation kernel machines, which belong to the class of interpolating classifiers, are capable of good generalization and have proven to be an effective substitute for support vector machines, particularly for graph classification. In this work, we further enhance their performance by studying multiple kernel learning. To this end, we propose a general scheme of polynomial combined kernel functions, employing both quadratic and cubic kernel combinations in our experimental work. Our findings demonstrate that this approach improves performance compared to individual graph kernels. Our work supports the use of interpolation kernel machines as an alternative to support vector machines, thereby contributing to greater methodological diversity.
Keyword:
Interpolation kernel machine
Multiple kernel leaning
Polynomial
Graph classification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
7.9K
被引数:
1.6W
机构
引用论文
.png){kind=icon}
分享收藏Report on the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3)
OAAI
OAAI分享收藏
查看更多内容