Design of kernels for support multivector machines involving the Clifford geometric product and the conformal geometric neuron
Design of kernels for support multivector machines involving the Clifford geometric product and the conformal geometric neuron
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DOI：
10.1109/ijcnn.2003.1224030
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发表时间：
2003-07
期刊：
影响因子：
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通讯作者：
E. Bayro-Corrochano;Nancy Arana;R. Vallejo
中科院分区：
文献类型：
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作者：
E. Bayro-Corrochano;Nancy Arana;R. Vallejo
提出了一种基于Clifford几何代数框架的非线性支持向量机核函数的设计方法。在这项研究中，我们提出的设计核涉及Clifford或几何产品利用非线性映射映射到高维几何代数的多个向量。我们还介绍了共形几何神经元的几何分类。实验证明了该方法的有效性。
This paper presents the design of kernels for nonlinear support vector machines using the Clifford geometric algebra framework. In this study we present the design of kernels involving the Clifford or geometric product making use of nonlinear mappings which map multi-vectors into higher dimensional geometric algebra. We introduce also the conformal geometric neuron for geometric classification. Experiments are given to demonstrate the usefulness of the approach.