手写体数字识别系统通过OCON神经网络通过剪枝选择性更新

Q4 Computer Science 模式识别与人工智能 Pub Date : 1992-08-30 DOI:10.1109/ICPR.1992.201862

Shuh-Chuan Tsay, Peir-Ren Hong, Bin-Chang Chieu

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引用次数: 6

摘要

使用OCON (one-class-one-net)网络和PSU (pruning selective update)训练算法进行手写数字识别。OCON网络结构的主要特点是整个网络由单输出多层感知机组成，每个子网代表一个类。设计了基于新代价函数的PSU训练算法，提高了训练速度。结果表明，采用新训练算法的OCON网络优于传统的反向传播算法。

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Handwritten digits recognition system via OCON neural network by pruning selective update

Performs the handwritten digits recognition using the OCON (one-class-one-net) network and the PSU (pruning selective update) training algorithm. The main feature of the architecture of OCON network is that the entire network is composed of single output multi-layer perceptron and each of the subnets represents one class. The PSU training algorithm defined on the new cost function is designed to speed up the training procedure. It is shown that an OCON network with the new training algorithm outperforms the conventional back-propagation algorithm.<>

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