Learning to Find Graph Pre-images
作者： Gökhan H. Bakır , Alexander Zien , Koji Tsuda
DOI: 10.1007/978-3-540-28649-3_31
关键词:
摘要: The recent development of graph kernel functions has made it possible to apply well-established machine learning methods graphs. However, allow for analyses that yield a as result, is necessary solve the so-called pre-image problem: reconstruct from its feature space representation induced by kernel. Here, we suggest practical solution this problem.
springer.com
core.ac.uk
mpg.de
sci-hub.st 参考文章(8)
Stefan Kamphausen, Nils Höltge, Frank Wirsching, Corinna Morys-Wortmann, Daniel Riester, Ruediger Goetz, Marcel Thürk, Andreas Schwienhorst, Genetic algorithm for the design of molecules with desired properties. Journal of Computer-aided Molecular Design. ,vol. 16, pp. 551- 567 ,(2002) , 10.1023/A:1021928016359
Koji Tsuda, Akihiro Inokuchi, Hisashi Kashima, Marginalized kernels between labeled graphs international conference on machine learning. pp. 321- 328 ,(2003)
Asim Kumar Debnath, Rosa L. Lopez de Compadre, Gargi Debnath, Alan J. Shusterman, Corwin Hansch, Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity. Journal of Medicinal Chemistry. ,vol. 34, pp. 786- 797 ,(1991) , 10.1021/JM00106A046
Xiaoyi Jiang, A. Munger, H. Bunke, An median graphs: properties, algorithms, and applications IEEE Transactions on Pattern Analysis and Machine Intelligence. ,vol. 23, pp. 1144- 1151 ,(2001) , 10.1109/34.954604
Bernhard Schölkopf, Jason Weston, Gökhan H. Bakir, Learning to Find Pre-Images neural information processing systems. ,vol. 16, pp. 449- 456 ,(2003)
H. Maarten Vinkers, Marc R. de Jonge, Frederik F. D. Daeyaert, Jan Heeres, Lucien M. H. Koymans, Joop H. van Lenthe, Paul J. Lewi, Henk Timmerman, Koen Van Aken, Paul A. J. Janssen, SYNOPSIS: SYNthesize and OPtimize System in Silico Journal of Medicinal Chemistry. ,vol. 46, pp. 2765- 2773 ,(2003) , 10.1021/JM030809X
Thorsten Joachims, Text Categorization with Suport Vector Machines: Learning with Many Relevant Features european conference on machine learning. ,vol. 1398, pp. 137- 142 ,(1998) , 10.1007/BFB0026683
B Scholkopfand, A Smola, Learning with Kernels european conference on machine learning. ,(2002)