Casali Daniele, Costantini Giovanni, Perfetti Renzo, Ricci Elisa
Department of Electronic Engineering, University of Rome "Tor Vergata," 00100 Rome, Italy.
IEEE Trans Neural Netw. 2006 Sep;17(5):1165-74. doi: 10.1109/TNN.2006.877539.
The relation existing between support vector machines (SVMs) and recurrent associative memories is investigated. The design of associative memories based on the generalized brain-state-in-a-box (GBSB) neural model is formulated as a set of independent classification tasks which can be efficiently solved by standard software packages for SVM learning. Some properties of the networks designed in this way are evidenced, like the fact that surprisingly they follow a generalized Hebb's law. The performance of the SVM approach is compared to existing methods with nonsymmetric connections, by some design examples.
研究了支持向量机（SVM）与递归联想记忆之间存在的关系。基于广义盒中脑状态（GBSB）神经模型的联想记忆设计被表述为一组独立的分类任务，这些任务可以通过用于SVM学习的标准软件包有效地解决。通过一些设计示例，证明了以这种方式设计的网络的一些特性，例如令人惊讶的是它们遵循广义赫布定律。将SVM方法的性能与具有非对称连接的现有方法进行了比较。
