Learning Complex Boolean Functions: Algorithms and Applications
Learning Complex Boolean Functions: Algorithms and Applications
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学习复杂的布尔函数：算法和应用
DOI：
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发表时间：
1993
期刊：
影响因子：
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通讯作者：
A. Sangiovanni
中科院分区：
文献类型：
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作者：
Arlindo L. Oliveira;A. Sangiovanni
最常用的神经网络模型不太适合直接的数字实现，因为每个节点需要在浮点值之间执行大量的操作。幸运的是，从实例中学习和泛化的能力并不局限于这种类型的网络。实际上，每个节点实现一个简单布尔函数的网络（布尔网络）可以设计成具有类似属性的方式。给出了从实例中生成布尔网络的两种算法。结果表明，这些算法对一类接受紧布尔网络描述的问题有很好的泛化效果。所描述的技术是通用的，可以应用于不知道具有该特性的任务。给出了两个应用实例：图像重建和手写字符识别。
The most commonly used neural network models are not well suited to direct digital implementations because each node needs to perform a large number of operations between floating point values. Fortunately, the ability to learn from examples and to generalize is not restricted to networks of this type. Indeed, networks where each node implements a simple Boolean function (Boolean networks) can be designed in such a way as to exhibit similar properties. Two algorithms that generate Boolean networks from examples are presented. The results show that these algorithms generalize very well in a class of problems that accept compact Boolean network descriptions. The techniques described are general and can be applied to tasks that are not known to have that characteristic. Two examples of applications are presented: image reconstruction and hand-written character recognition.