An application of SVM: alphanumeric character recognition

International 1989 Joint Conference on Neural Networks Pub Date : 1900-01-01 DOI:10.1109/IJCNN.1989.118320

Y. Kato, H. Saito, T. Ejima

引用次数: 4

Abstract

Summary form only given. The application of a stochastic vector machine (SVM) to alphanumeric character recognition is considered. The SVM is a new multilayered network with learning ability as in the backpropagation (BP) model. The system dynamics in the network is represented on the direct product space of the stochastic vector, so the network consists of units and states. The learning rule follows gradient decent formulation so as to minimize Kullback divergence between the output and the desired states. A preliminary recognition experiment on alphabetic characters was conducted, and SVM's internal representations were examined from weight patterns in the network. The experiment indicates that distributed or local representation is developed by the learning algorithm. A network system was constructed and applied to alphanumeric character recognition. Experimental results indicate that the SVM can perform as well as the BP model.<>

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支持向量机的一个应用:字母数字字符识别

只提供摘要形式。研究了随机向量机在字母数字字符识别中的应用。支持向量机是一种具有BP模型学习能力的新型多层网络。网络中的系统动力学用随机向量的直积空间表示，因此网络由单元和状态组成。学习规则遵循梯度体面公式，使输出与期望状态之间的Kullback散度最小。对字母字符进行了初步的识别实验，并从网络中的权值模式对支持向量机的内部表示进行了检验。实验表明，该学习算法实现了分布式或局部表示。构建了一个网络系统，并将其应用于字母数字字符识别。实验结果表明，支持向量机的性能与BP模型相当。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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International 1989 Joint Conference on Neural Networks

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