首页|Simula Research Laboratory Reports Findings in Machine Learning (AABBA Graph Ker nel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning)
Simula Research Laboratory Reports Findings in Machine Learning (AABBA Graph Ker nel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to newsreporting from Oslo, Norway, by NewsRx journalists, research stated, “Graphs are one of the most naturaland powerful r epresentations available for molecules; natural because they have an intuitive c orrespondenceto skeletal formulas, the language used by chemists worldwide, and powerful, because they are highlyexpressive both globally (molecular topology) and locally (atom and bond properties). Graph kernels are used to transform mol ecular graphs into fixed-length vectors, which, based on their capacity of measu ringsimilarity, can be used as fingerprints for machine learning (ML).”
NETL
NSTL
