Geometric neural networks and support multi-vector machines

被引：5

作者：

Bayro-Corrochano, E ^{[1
]}

Vallejo, R ^{[1
]}

机构：

[1] CIMAT, Dept Comp Sci, Ctr Invest Matemat, Guadalajara 36000, Jalisco, Mexico

来源：

IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL VI | 2000年

关键词：

D O I：

10.1109/IJCNN.2000.859426

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The representation of the external world in biological creatures appears to be defined in terms of geometry. In this regard the author uses the Clifford geometric algebra for the development of geometric type neural networks. The contribution of this paper is the extension of our past work including the use of the SV-Machines in the Clifford algebra framework. Thus geometric MLPs and RBF networks can be generated using SV-Machines straightforwardly. In this way we expanded the sphere of applicability of the SV-Machines by the treatment of multivectors which encode the geometry of the data manifold in a rich manner.

引用

页码：389 / 394

页数：6

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