An introduction to recursive neural networks and kernel methods for cheminformatics

被引：8
|
作者：
Micheli, Alessio
Sperduti, Alessandro
Starita, Antonina
机构：
[1] Univ Pisa, Dipartimento Informat, I-56127 Pisa, Italy
[2] Univ Padua, Dipartimento Matemat Pura & Applicata, I-35131 Padua, Italy
来源：
CURRENT PHARMACEUTICAL DESIGN | 2007年 / 13卷 / 14期
关键词：
QSAR/QSPR; recursive neural networks; kernel for structures; kernel methods; learning in structured domains; machine learning;
D O I：
10.2174/138161207780765981
中图分类号：
R9 [药学];
学科分类号：
1007 ;
摘要：
The aim of this paper is to introduce the reader to new developments in Neural Networks and Kernel Machines concerning the treatment of structured domains. Specifically, we discuss the research on these relatively new models to introduce a novel and more general approach to QSPR/QSAR analysis. The focus is on the computational side and not on the experimental one.
引用
收藏
页码：1469 / 1495
页数：27
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